Methods and materials for identifying metastatic malignant skin lesions and treating skin cancer

ABSTRACT

This document provides methods and materials for identifying metastatic malignant skin lesions (e.g., malignant pigmented skin lesions). For example, methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify metastatic malignant skin lesions are provided. This document also provides methods and materials for treating skin cancer. For example, methods and materials for identifying a mammal (e.g., a human) having a pre-metastatic skin lesion (e.g., pre-metastatic melanoma) and treating that mammal with pentamidine (4,4′-[pentane-1,5-diylbis(oxy)]dibenzenecarboximidamide) are provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Ser. No. 62/037,325, filed Aug. 14, 2014, and U.S. Provisional Ser. No. 62/142,831, filed Apr. 3, 2015. This disclosure of the prior applications are considered part of (and are incorporated by reference in) the disclosure of this application.

BACKGROUND

1. Technical Field

This document relates to methods and materials for identifying metastatic malignant skin lesions (e.g., metastatic malignant pigmented skin lesions). For example, this document relates to methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify metastatic malignant skin lesions. This document also relates to methods and materials for treating skin cancer. For example, this document relates to methods and materials for identifying a mammal (e.g., a human) having a pre-metastatic skin lesion (e.g., pre-metastatic melanoma) and treating that mammal with pentamidine (4,4′-[pentane-1,5-diylbis(oxy)]dibenzenecarboximidamide).

2. Background Information

Malignant skin lesions are typically identified by obtaining a skin biopsy and morphologically assessing the biopsy's melanocytes under a microscope. Such a procedure can be difficult to standardize and can lead to overcalling of melanomas.

Once a diagnosis of melanoma is made by morphological assessment, the risk of metastasis is typically determined by the invasion depth of malignant cells into the skin (i.e., the Breslow depth). The Breslow depth can dictate further work-up such as a need for an invasive sentinel lymph node (SLN) procedure. Such procedures, however, can lead to inaccurate determinations of the true malignant potential of a pigmented lesion.

SUMMARY

This document provides methods and materials for identifying metastatic malignant skin lesions (e.g., metastatic malignant pigmented skin lesions). For example, this document provides methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify metastatic malignant skin lesions.

As described herein, quantitative PCR can be performed using a routine skin biopsy sample (e.g., a paraffin-embedded tissue biopsy) to obtain expression data (e.g., gene copy numbers) for one or more marker genes. Correction protocols can be used to reduce the impact of basal keratinocyte contamination on the analysis of the expression data from the test sample. For example, the contribution of gene expression from basal keratinocytes present within the test skin sample can be determined and removed from the overall gene expression values to determine the final gene expression value for a particular gene as expressed from cells other than basal keratinocytes (e.g., melanocytes). An assessment of the final gene expression values, which include minimal, if any, contribution from basal keratinocytes, for a collection of marker genes can be used to determine the benign or malignant or metastatic biological behavior of the tested skin lesion.

This document also provides methods and materials for treating skin cancer. For example, this document provides methods and materials for identifying a mammal (e.g., a human) having a pre-metastatic skin lesion (e.g., pre-metastatic melanoma) and treating that mammal with pentamidine.

As described herein, aggressive cancer cells (e.g., melanoma cells) can remodel their cell adhesion structures (e.g., osteopontin (SPP1) polypeptides) to invade tissues and metastasize. Screening over 1,200 compounds for the ability to reduce expression of SPP1 polypeptides resulted in the identification of pentamidine as an effective agent for disrupting integrin adhesion remodeling, thereby demonstrating that pentamidine can be used to reduce or inhibit cancer progression at an early stage (e.g., prior to metastatic cancer). In some cases, a mammal (e.g., a human) identified as having skin cancer cells that express an elevated level of PLAT, ITGB3, LAMB1, and/or TP53 can be administered pentamidine to reduce or inhibit cancer progression. For example, pentamidine can be administered to a mammal (e.g., a human) having pre-metastatic melanoma cells that were determined to have an elevated level of PLAT, ITGB3, LAMB1, and/or TP53 expression. In such cases, the mammal being treated with pentamidine may not experience cancer progression from the pre-metastatic melanoma state to a metastatic melanoma state.

In general, one aspect of this document features a method for identifying a metastatic malignant skin lesion. The method comprises, or consists essentially of, (a) determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, ITGB3, LAMB1, and TP53 to obtain a measured expression level of the marker gene for the test sample, (b) determining, within the test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of the keratinocyte marker gene for the test sample, (c) removing, from the measured expression level of the marker gene for the test sample, a level of expression attributable to keratinocytes present in the test sample using the measured expression level of the keratinocyte marker gene for the test sample and a keratinocyte correction factor to obtain a corrected value of marker gene expression for the test sample, and (d) identifying the test sample as containing a metastatic malignant skin lesion based, at least in part, on the corrected value of marker gene expression for the test sample. The keratinocyte marker gene can be K14. The marker gene can be PLAT or ITGB3. The step (c) can comprise (i) multiplying the measured expression level of the keratinocyte marker gene for the test sample by the keratinocyte correction factor to obtain a correction value and (ii) subtracting the correction value from the measured expression level of the marker gene for the test sample to obtain the corrected value of marker gene expression for the test sample. The method can comprise determining, within the test sample, the expression level of at least two marker genes selected from the group consisting of PLAT, ITGB3, LAMB1, and TP53 to obtain measured expression levels of the at least two marker genes for the test sample. The method can comprise determining, within the test sample, the expression level of at least three marker genes selected from the group consisting of PLAT, ITGB3, LAMB1, and TP53 to obtain measured expression levels of the at least three marker genes for the test sample. The method can comprise determining, within the test sample, the expression level of PLAT, ITGB3, LAMB1, and TP53 to obtain measured expression levels of the PLAT, ITGB3, LAMB1, and TP53 for the test sample.

In another aspect, this document features a kit for identifying a metastatic malignant skin lesion. The kit comprises, or consists essentially of, (a) a primer pair for determining, within a test sample, the expression level of a marker gene selected from the group consisting of LAMB1 and TP53 to obtain a measured expression level of the marker gene for the test sample, and (b) a primer pair for determining, within the test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of the keratinocyte marker gene for the test sample. The keratinocyte marker gene can be K14. The marker gene can be LAMB1. The marker gene can be TP53. The kit can comprise primer pairs for determining, within the test sample, the expression level of LAMB1 and TP53 to obtain measured expression levels of the LAMB1 and TP53 for the test sample. The kit can comprise primer pairs for determining, within the test sample, the expression level of PLAT to obtain measured expression levels of the PLAT for the test sample. The kit can comprise primer pairs for determining, within the test sample, the expression level of ITGB3 to obtain measured expression levels of the ITGB3 for the test sample. The kit can comprise primer pairs for determining, within the test sample, the expression level of PLAT and ITGB3 to obtain measured expression levels of the ITGB3 and PLAT for the test sample.

In another aspect, this document features a method for identifying a metastatic malignant skin lesion. The method comprises, or consists essentially of, (a) determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, ITGB3, LAMB1, and TP53 to obtain a measured expression level of the marker gene for the test sample, (b) determining, within the test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of the keratinocyte marker gene for the test sample, (c) removing, from the measured expression level of the marker gene for the test sample, a level of expression attributable to keratinocytes present in the test sample using the measured expression level of the keratinocyte marker gene for the test sample and a keratinocyte correction factor to obtain a corrected value of marker gene expression for the test sample, and (d) identifying the test sample as containing a metastatic malignant skin lesion based, at least in part, on the corrected value of marker gene expression for the test sample. The keratinocyte marker gene can be K14. The marker gene can be LAMB1 or TP53. The step (c) can comprise (i) multiplying the measured expression level of the keratinocyte marker gene for the test sample by the keratinocyte correction factor to obtain a correction value and (ii) subtracting the correction value from the measured expression level of the marker gene for the test sample to obtain the corrected value of marker gene expression for the test sample.

In another aspect, this document features a method for identifying a pre-metastatic skin lesion having an increased likelihood of metastasizing. The method comprises, or consists essentially of, (a) detecting the presence of an elevated level of PLAT, ITGB3, LAMB1, and TP53 expression in cells of the pre-metastatic skin lesion, and (d) classifying the pre-metastatic skin lesion as having an increased likelihood of metastasizing based, at least in part, on the presence. The method can comprise measuring the levels of PLAT, ITGB3, LAMB1, and TP53 expression in cells of the pre-metastatic skin lesion and performing an analysis using a 2 trees, 2 leaves model. The pre-metastatic skin lesion can be a human pre-metastatic skin lesion.

In another aspect, this document features a method for treating skin cancer, wherein the method comprises, or consists essentially of, (a) detecting the presence of an elevated level of PLAT, ITGB3, LAMB1, and TP53 expression in skin cancer cells of a mammal, and (d) administering pentamidine to the mammal. The mammal can be a human. The skin cancer can be pre-metastatic skin cancer. The skin cancer can be pre-metastatic melanoma. Administration of the pentamidine can reduce the progression of the pre-metastatic melanoma to metastatic melanoma. The pre-metastatic melanoma can fail to progress to metastatic melanoma following administration of the pentamidine. The method can comprise measuring the levels of PLAT, ITGB3, LAMB1, and TP53 expression in cells of the skin cancer cells and performing an analysis using a 2 trees, 2 leaves model.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Other features and advantages of the invention will be apparent from the following detailed description, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flow chart of an exemplary process for determining the gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for a marker gene by cells within a tested sample (e.g., a tested skin biopsy sample).

FIG. 2 is a flow chart of an exemplary process for determining a keratinocyte correction factor for a marker gene of interest.

FIG. 3 is a flow chart of an exemplary process for removing copy number contamination from basal keratinocytes from a copy number value for a marker gene to determine the gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for that marker gene by cells within a tested sample (e.g., a tested skin biopsy sample).

FIG. 4 is a diagram of an example of a generic computer device and a generic mobile computer device that can be used as described herein.

FIG. 5 is a flow chart of an exemplary process for using FN1 and SPP1 expression levels to determine the benign or malignant nature of a skin lesion.

FIG. 6 is a flow chart of an exemplary process for using FN1 and ITGB3 expression levels to determine the benign or malignant nature of a skin lesion.

FIG. 7 is a network diagram.

FIG. 8. Logic regression. (A) The null model randomization test suggests a relationship exists between SLN positivity and the gene expression variables. The ‘best model’ and ‘null model’ reference lines mark the deviance scores for the best model fit to outcomes and the null model. The histogram shows the distribution of deviance scores for models fit against randomize outcomes. Since the best model outperforms the randomized outcome models there was a relationship between SLN positivity and gene expression. (B) Results from 10-fold cross validation results for models using 1, 2, and 3 trees with at most 6 binary variables or leaves. The label in each square denotes the number of trees used in the model. The scores on the y-axis are the deviance scores using the test data and the x-axis denotes the number of binary variables (leaves) used in each model. Notice that model the using 2 trees and 4 leaves had the best test score. (C) Summary of the permutation test results for 2 trees using 2 to 5 leaves. The two solid reference lines indicate the best deviance score and the null model deviance score. The dashed reference line represents the deviance score using a one tree model. The histogram summarizes the deviance scores using permuted outcomes. There were 1,000 model fits for each model size. Scores above the best model reference line indicate there were models that fit the permuted data better than the actual data. For the model with 2 tree and 5 leaves about 10% deviance scores for models fit using permuted data have a lower score than using the best model for the observed original data indicated by the left most vertical reference line. (D) The formulas for the best fitting models involved two trees with a model size of 4 or 5.

FIG. 9. Differential expression analysis by next-generation sequencing reveals that integrin adhesion genes are up-regulated in benign nevi vs. invasive melanoma. 160 of 15,413 genes were significantly regulated in benign nevi vs. invasive melanoma (FDR <0.01) as determined by next-generation sequencing (NGS). Functional relationships between these genes were mapped by the STRING database (http://string.embl.de). Two main functional clusters emerged; the largest was related to integrin-linked cell adhesion. Orange circles indicate gene up-regulation; blue circles indicate down-regulation. Numbers indicate fold-change, malignant over benign.

FIG. 10. Integrin adhesion genes are over-expressed in invasive melanoma vs. non-invasive precursor lesions. Confirmation of NGS results by quantitative PCR. Genes with significant regulation (p<0.001, Mann-Whitney U Test) are bolded and marked with an asterisk. FC, fold change; S, severe atypia.

FIG. 11. Integrin adhesion genes predict SLN metastasis. (A) Receiver operating characteristic curves for the 3 models in Table 16 using the model development cohort. (B) Summary of the sensitivity and specificity according to the predicted probability of a positive SLNB estimated from model C. (C) Nomogram for the predicting positive SLNB based on model C.

FIG. 12. IPTG-inducible shRNA effectively knocks-down FAK in a B-rafV600E melanoma cell line. (A) IPTG reduces FAK mRNA through shRNA 841 and 102 but not control shRNA (NC) in WM858 cells (mean±s.d.; n=4; *, p<0.05; **, p<0.005; ***, p<0.001). p values, Student's t-test; ns, not significant. (B) FAK shRNA 102 but not control shRNA (NC) reduces FAK protein levels in WM858 cells. IPTG treatment of shRNA-free normal WM858 cells (no shRNA) was without effect on FAK protein levels. (C-D) FAK could be visualized in focal adhesions in WM858 NC but not shRNA 102 cells after 0.05 mM IPTG for 5 days. (C) Triple staining DAPI; FAK; Paxillin (PAX) as a focal adhesion marker. (D) DAPI/FAK staining only. Bar, 50 μm.

FIG. 13. B-raf^(V600E) inhibits FAK to promote integrin surface expression. (A) shRNAs 841, 102 and NC were induced in WM858 cells by IPTG for 5 days (n=4) followed by RNA quantitation. Genes regulated at 0 vs. 0.05 mM IPTG in 841 and 102 but not NC cells are shown. These were: ITGB3 (orange), FAK (blue). Light orange, light blue: up- or downregulation, respectively, by either 841 or 102 shRNA. (B) WM858 cells were transfected with FAK or EGFP cDNA. Regulated genes, FAK over EGFP cells, are shown in orange (up) or blue (down) (n=4). (C) Flow cytometry of NC, 841 and 102 cells (un-induced vs. 0.05 mM IPTG for 5 days). (D) Integrin cell surface mean intensities; mean±s.d.; n=3; *, p<0.05; p values, Student's t-test; ns, not significant. (E) Visualization of focal adhesions by paxillin staining on micropatterned fibronectin disks. (F-G) Proliferation speed in the absence (F) or presence (G) of FAK shRNAs; mean±s.d.; n=8; *, p<0.05; p values, Student's t-test; ns, not significant. (H-I) Scratch wound healing in IPTG-induced cells; representative experiment (n=3). (J-K) Effect of IPTG-induced FAK knock-down on total (t) and phospho (p)-ERK; mean±s.d.; n=4; *, p<0.05; p values, Student's t-test. (L) Cell surface β1 and β3 integrin expression (flow cytometry) in B-raf^(V600E) and wild-type cells after overnight drug incubation; % expression relative to DMSO is shown. (M-O) FAK/ERK levels in NHM and WM858 after overnight drug incubation (M); quantification of FAK (N) and ERK phosphorylation (0); mean±s.d.; n=4; *, p<0.05; p values, Student's t-test. (P) ERK activity in NC, 841 and 102 cells (0.05 mM IPTG for 5 days) after overnight drug incubation.

FIG. 14. The luciferase construct for high-throughput screening. Genomic structure of the SPP1 gene is shown as well as a targeting approach. A DNA double-strand break was induced in exon (E) 2 of SPP1, 3′ of the ATG start codon by a custom-made zinc finger nuclease (ZFN), arrow. A targeting vector with 500 bp homology arms (HA, middle), Hygromycin resistance (HYGRO), a target promoter-driven firefly luciferase (LUC2P), and a CMV-pomoter driven renilla luciferase (HRLUC) was offered for repair at the time of the double-strand break. PA, PA terminator signal. Blue boxes, untranslated region; black boxes, translated region.

FIG. 15. Pentamidine inhibits SPP1 expression, proliferation, and invasion of melanoma cells. (A) WM858 cells with a luciferase-tagged SPP1 promoter were screened against a LOPAC. (B-D) Pentamidine inhibits SPP1 promoter activity in the Dual-Glo® assay (B), but also by quantitative PCR in normal WM858 (C) and M12 cells (D). (E) Pentamidine inhibits the expression of other adhesion molecules, i.e. β3 integrin (ITGB3) and t-PA (PLAT). (F-G) Pentamidine effectively inhibits M12 invasion into 2 mg/mL of Matrigel® (F). A visible reduction in Matrigel invasion is observed that exceeds the effects of B-raf inhibition (G); blue, area of scratch wound at time zero; yellow line, invasion front; red, RFP-labeled M12 nuclei on phase contrast background. (H) Image of a female nude mouse harboring an intradermal M12 PDX. (I) H&E stained cryosection of an untreated M12 PDX. (J) Pentamidine injections reduce SPP1, β3 integrin, and t-PA (PLAT) mRNA expression in M12 PDX. Average of 3 mice is shown. PENTA, Pentamidine; DABRA, Dabrafenib. WM858 and M12 are B-rafV600E metastatic melanoma cells.

FIG. 16. Differential gene expression by NGS in a cohort of 4 patients with primary skin melanoma that had not metastasized (median Breslow depth: 2.6 mm) and 3 patients that had metastasized regionally (median Breslow depth: 2.3 mm). Out of a total of 15,196 genes, 208 genes were identified with a FDR <0.01. ITGB3 as well as SRC, a downstream effector of β3 integrin, 1 formed the center of a functional network deregulated in regionally metastatic vs. non-metastatic melanoma. Genes (nodes) functionally disconnected to any of the other genes were hidden. Functional relationships between genes are indicated by lines and were plotted using the STRING database (http://string.embl.de).

FIG. 17. Integrin cell adhesion is a cellular system differentially expressed in metastatic melanoma vs. non-metastatic pigmented lesions. 164 of 16,029 genes were significantly regulated in benign nevi vs. regionally metastatic melanoma (FDR <0.01) as determined by next-generation sequencing (NGS). Functional relationships between these genes were mapped by the STRING database (http://string.embl.de). Genes without known functional relationships to other genes (i.e. disconnected nodes) or networks with <3 genes were hidden. A large cluster emerged that was functionally related to integrin cell adhesion and the extracellular space (ECM). Additional NGS-based comparison of samples from patients with regional metastasis vs. non-metastasic melanoma revealed the deregulation of a ITGB3/protein kinase C/SRC network in regionally metastatic melanoma. Orange circles indicate gene up-regulation, regionally metastatic melanoma vs. nevi; blue circles indicate down-regulation, regionally metastatic melanoma vs. nevi. Orange rings indicate up-regulation, regionally metastatic vs. non-metastatic melanoma.

DETAILED DESCRIPTION

This document provides methods and materials for identifying metastatic malignant skin lesions (e.g., metastatic malignant pigmented skin lesions). For example, this document provides methods and materials for using quantitative PCR results and correction protocols to reduce the impact of basal keratinocyte contamination on the analysis of test sample results to identify metastatic malignant skin lesions.

FIG. 1 shows an exemplary process 100 for determining a gene expression value, which includes minimal, if any, contribution from basal keratinocytes, for a marker gene by cells within a tested sample (e.g., a tested skin biopsy sample). The process begins at box 102, where quantitative PCR using a collection of primer sets and a test sample is used to obtain a Ct value for the target of each primer set. Each gene of interest can be assessed using a single primer set or multiple different primer sets (e.g., two, three, four, five, six, seven, or more different primer sets). In some cases, quantitative PCR is performed using each primer set and control nucleic acid of the target of each primer set (e.g., linearized cDNA fragments) to obtain a standard curve for each primer set as set forth in box 104. In some cases, quantitative PCR is performed using each primer set and a known sample as an internal control (e.g., a stock biological sample) to obtain an internal control value for each primer set as set forth in box 106. This internal control can be used to set values for each primer set across different assays. In some cases, the quantitative PCR performed according to boxes 102, 104, and 106 can be performed in parallel. For example, the quantitative PCR performed according to boxes 102, 104, and 106 can be performed in a single 96 well format.

At box 108, the quality of the obtained standard curves can be confirmed. In some cases, a gene of interest included in the assay format can be a melanocyte marker (e.g., levels of MLANA and/or MITF expression) to confirm the presence of melanocytes in the test sample. Other examples of melanocyte markers that can be used as described herein include, without limitation, TYR, TYRP1, DCT, PMEL, OCA2, MLPH, and MC1R.

At box 110, the raw copy number of each target present in the test sample is determined using the Ct values and the standard curve for each target. In some cases, the averaged, corrected copy number for each gene is calculated using the raw copy number of each target of a particular gene and the internal control value for each primer set (box 112). This averaged, corrected copy number value for each gene can be normalized to a set number of one or more housekeeping genes as set forth in box 114. For example, each averaged, corrected copy number value for each gene can be normalized to 100,000 copies of the combination of ACTB, RPL8, RPLP0, and B2M. Other examples of housekeeping genes that can be used as described herein include, without limitation, RRN18S, GAPDH, PGK1, PPIA, RPL13A, YWHAZ, SDHA, TFRC, ALAS1, GUSB, HMBS, HPRT1, TBP, CLTC, MRFAP1, PPP2CA, PSMA1, RPL13A, RPS29, SLC25A3, TXNL1, and TUPP. Once normalized, the copy number values for each gene can be referred to as the averaged, corrected, normalized copy number for that gene as present in the test sample.

At box 116, the averaged, corrected, normalized copy number for each gene can be adjusted to remove the copy number contamination from basal keratinocytes present in the test sample. In general, copy number contamination from basal keratinocytes can be removed by (a) determining a keratinocyte correction factor for the gene of interest using one or more keratinocyte markers (e.g., keratin 14 (K14)) and one or more normal skin samples (e.g., FFPE-embedded normal skin samples), (b) determining the averaged, corrected, normalized copy number value for the one or more keratinocyte markers of the test sample and multiplying that value by the keratinocyte correction factor to obtain a correction value for the gene of interest, and (c) subtracting that correction value from the averaged, corrected, normalized copy number value of the gene of interest to obtain the final copy number for the gene of interest. Examples of keratinocyte markers that can be used as described herein include, without limitation, KRT5, KRT1, KRT10, KRT17, ITGB4, ITGA6, PLEC, DST, and COL17A1.

With reference to FIG. 2, process 200 can be used to obtain a keratinocyte correction factor for a gene of interest. At box 202, the averaged, corrected, normalized copy number for one or more genes of interest (e.g., Gene X) and one or more basal keratinocyte marker genes (e.g., K14) are determined using one or more normal skin samples and procedures similar to those described in FIG. 1. As box 204, the keratinocyte correction factor for each gene of interest (e.g., Gene X) is determined by dividing the averaged, corrected, normalized copy number for each gene of interest present in a normal skin sample by the averaged, corrected, normalized copy number of a basal keratinocyte marker gene present in a normal skin sample. Examples of keratinocyte correction factors for particular genes of interest are set forth in Table E under column “AVG per copy K14.”

With reference to FIG. 3, once a keratinocyte correction factor in determined for a particular gene of interest (e.g., Gene X), then the averaged, corrected, normalized copy number for the basal keratinocyte marker gene present in the test sample can be multiplied by the keratinocyte correction factor for the gene of interest (e.g., Gene X) to obtain a correction value for the gene of interest (e.g., Gene X). See, e.g., box 302. At box 304, the correction value for the gene of interest (e.g., Gene X) is subtracted from the averaged, corrected, normalized copy number for the gene of interest (e.g., Gene X) present in the test sample to obtain a final copy number value of the gene of interest (e.g., Gene X) present in the test sample.

FIG. 4 is a diagram of an example of a generic computer device 1400 and a generic mobile computer device 1450, which may be used with the techniques described herein. Computing device 1400 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Computing device 1450 is intended to represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smart phones, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed in this document.

Computing device 1400 includes a processor 1402, memory 1404, a storage device 1406, a high-speed interface 1408 connecting to memory 1404 and high-speed expansion ports 1410, and a low speed interface 1415 connecting to low speed bus 1414 and storage device 1406. Each of the components 1402, 1404, 1406, 1408, 1410, and 1415, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor 1402 can process instructions for execution within the computing device 1400, including instructions stored in the memory 1404 or on the storage device 1406 to display graphical information for a GUI on an external input/output device, such as display 1416 coupled to high speed interface 1408. In other implementations, multiple processors and/or multiple buses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple computing devices 1400 may be connected, with each device providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).

The memory 1404 stores information within the computing device 1400. In one implementation, the memory 1404 is a volatile memory unit or units. In another implementation, the memory 1404 is a non-volatile memory unit or units. The memory 1404 may also be another form of computer-readable medium, such as a magnetic or optical disk.

The storage device 1406 is capable of providing mass storage for the computing device 1400. In one implementation, the storage device 1406 may be or contain a computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. A computer program product can be tangibly embodied in an information carrier. The computer program product may also contain instructions that, when executed, perform one or more methods, such as those described herein. The information carrier is a computer- or machine-readable medium, such as the memory 1404, the storage device 1406, memory on processor 1402, or a propagated signal.

The high speed controller 1408 manages bandwidth-intensive operations for the computing device 1400, while the low speed controller 1415 manages lower bandwidth-intensive operations. Such allocation of functions is exemplary only. In one implementation, the high-speed controller 1408 is coupled to memory 1404, display 1416 (e.g., through a graphics processor or accelerator), and to high-speed expansion ports 1410, which may accept various expansion cards (not shown). In the implementation, low-speed controller 1415 is coupled to storage device 1406 and low-speed expansion port 1414. The low-speed expansion port, which may include various communication ports (e.g., USB, Bluetooth, Ethernet, or wireless Ethernet) may be coupled to one or more input/output devices, such as a keyboard, a pointing device, a scanner, an optical reader, a fluorescent signal detector, or a networking device such as a switch or router, e.g., through a network adapter.

The computing device 1400 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a standard server 1420, or multiple times in a group of such servers. It may also be implemented as part of a rack server system 1424. In addition, it may be implemented in a personal computer such as a laptop computer 1422. In some cases, components from computing device 1400 may be combined with other components in a mobile device (not shown), such as device 1450. Each of such devices may contain one or more of computing device 1400, 1450, and an entire system may be made up of multiple computing devices 1400, 1450 communicating with each other.

Computing device 1450 includes a processor 1452, memory 1464, an input/output device such as a display 1454, a communication interface 1466, and a transceiver 1468, among other components (e.g., a scanner, an optical reader, a fluorescent signal detector). The device 1450 may also be provided with a storage device, such as a microdrive or other device, to provide additional storage. Each of the components 1450, 1452, 1464, 1454, 1466, and 1468, are interconnected using various buses, and several of the components may be mounted on a common motherboard or in other manners as appropriate.

The processor 1452 can execute instructions within the computing device 1450, including instructions stored in the memory 1464. The processor may be implemented as a chipset of chips that include separate and multiple analog and digital processors. The processor may provide, for example, for coordination of the other components of the device 1450, such as control of user interfaces, applications run by device 1450, and wireless communication by device 1450.

Processor 1452 may communicate with a user through control interface 1458 and display interface 1456 coupled to a display 1454. The display 1454 may be, for example, a TFT LCD (Thin-Film-Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface 1456 may comprise appropriate circuitry for driving the display 1454 to present graphical and other information to a user. The control interface 1458 may receive commands from a user and convert them for submission to the processor 1452. In addition, an external interface 1462 may be provide in communication with processor 1452, so as to enable near area communication of device 1450 with other devices. External interface 1462 may provide, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.

The memory 1464 stores information within the computing device 1450. The memory 1464 can be implemented as one or more of a computer-readable medium or media, a volatile memory unit or units, or a non-volatile memory unit or units. Expansion memory 1474 may also be provided and connected to device 1450 through expansion interface 1472, which may include, for example, a SIMM (Single In Line Memory Module) card interface. Such expansion memory 1474 may provide extra storage space for device 1450, or may also store applications or other information for device 1450. For example, expansion memory 1474 may include instructions to carry out or supplement the processes described herein, and may include secure information also. Thus, for example, expansion memory 1474 may be provide as a security module for device 1450, and may be programmed with instructions that permit secure use of device 1450. In addition, secure applications may be provided via the SIMM cards, along with additional information, such as placing identifying information on the SIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory, as discussed below. In one implementation, a computer program product is tangibly embodied in an information carrier. The computer program product contains instructions that, when executed, perform one or more methods, such as those described herein. The information carrier is a computer- or machine-readable medium, such as the memory 1464, expansion memory 1474, memory on processor 1452, or a propagated signal that may be received, for example, over transceiver 1468 or external interface 1462.

Device 1450 may communicate wirelessly through communication interface 1466, which may include digital signal processing circuitry where necessary. Communication interface 1466 may provide for communications under various modes or protocols, such as GSM voice calls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others. Such communication may occur, for example, through radio-frequency transceiver 1468. In addition, short-range communication may occur, such as using a Bluetooth, WiFi, or other such transceiver (not shown). In addition, GPS (Global Positioning System) receiver module 1470 may provide additional navigation- and location-related wireless data to device 1450, which may be used as appropriate by applications running on device 1450.

Device 1450 may also communicate audibly using audio codec 1460, which may receive spoken information from a user and convert it to usable digital information. Audio codec 1460 may likewise generate audible sound for a user, such as through a speaker, e.g., in a handset of device 1450. Such sound may include sound from voice telephone calls, may include recorded sound (e.g., voice messages, music files, etc.) and may also include sound generated by applications operating on device 1450.

The computing device 1450 may be implemented in a number of different forms, as shown in the figure. For example, it may be implemented as a cellular telephone 1480. It may also be implemented as part of a smartphone 1482, personal digital assistant, or other similar mobile device.

Various implementations of the systems and techniques described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described herein can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (“LAN”), a wide area network (“WAN”), and the Internet.

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

This document also provides methods and materials involved in treating mammals having skin cancer (e.g., melanoma such as pre-metastatic melanoma) by administering pentamidine to the mammal. Any appropriate mammal having skin cancer can be treated as described herein. For example, humans and other primates such as monkeys having skin cancer can be treated with pentamidine. In some cases, dogs, cats, horses, bovine species, porcine species, mice, or rats can be treated with pentamidine as described herein. In addition, a mammal having any particular type of skin cancer can be treated as described herein. For example, a mammal having melanoma, pre-metastatic melanoma, locally metastatic melanoma (i.e., skin in close proximity to primary melanoma), regionally metastatic melanoma (e.g., metastases to regional sentinel lymph nodes), or distant metastases (e.g., metastases to internal organs) can be treated with pentamidine as described herein. In some cases, a mammal determined to have skin cancer cells that express an elevated level of one or more marker genes described herein (e.g., PLAT, ITGB3, LAMB1, and/or TP53) can be treated with pentamidine. In some cases, a mammal (e.g., a human) determined to have skin cancer cells that express an elevated level of one or more marker genes (e.g., PLAT, ITGB3, LAMB1, and/or TP53) using the methods or materials provided herein can be treated with pentamidine.

Any appropriate method can be used to identify a mammal having skin cancer (e.g., pre-metastatic melanoma) that can be treated using pentamidine. For example, imaging, biopsy, pathology, PCR, and sequencing techniques can be used to identify a human having skin cancer cells that express an elevated level of PLAT, ITGB3, LAMB1, and/or TP53.

Once identified as having skin cancer or skin cancer that expresses an elevated level of PLAT, ITGB3, LAMB1, and/or TP53, the mammal can be administered pentamidine. In some cases, pentamidine can be administered in combination with a chemotherapeutic agent to treat skin cancer (e.g., pre-metastatic melanoma). Examples of chemotherapeutic agents that can be used in combination with pentamidine include, without limitation, taxane therapies, anthracycline therapies, and gemcitabine therapies. Examples of taxane therapies include, without limitation, cancer treatments that involve administering taxane agents such as paclitaxel, docetacel, or other microtubule disrupting agents such as vinblastine, vincristine, or vinorelbine. In some cases, drugs used to treat gout or chochicine can be used as described herein to treat a mammal having skin cancer. Examples of anthracycline therapies include, without limitation, cancer treatments that involve administering anthracycline agents such as doxorubicine, daunorubicin, epirubicin, idarubicin, valrubicin, or mitoxantrone.

In some cases, pentamidine can be formulated into a pharmaceutically acceptable composition for administration to a mammal having skin cancer (e.g., pre-metastatic melanoma). For example, a therapeutically effective amount of pentamidine can be formulated together with one or more pharmaceutically acceptable carriers (additives) and/or diluents. A pharmaceutical composition can be formulated for administration in solid or liquid form including, without limitation, sterile solutions, suspensions, sustained-release formulations, tablets, capsules, pills, powders, and granules.

Pharmaceutically acceptable carriers, fillers, and vehicles that may be used in a pharmaceutical composition described herein include, without limitation, ion exchangers, alumina, aluminum stearate, lecithin, serum proteins, such as human serum albumin, buffer substances such as phosphates, glycine, sorbic acid, potassium sorbate, partial glyceride mixtures of saturated vegetable fatty acids, water, salts or electrolytes, such as protamine sulfate, disodium hydrogen phosphate, potassium hydrogen phosphate, sodium chloride, zinc salts, colloidal silica, magnesium trisilicate, polyvinyl pyrrolidone, cellulose-based substances, polyethylene glycol, sodium carboxymethylcellulose, polyacrylates, waxes, polyethylene-polyoxypropylene-block polymers, polyethylene glycol and wool fat.

A pharmaceutical composition containing pentamidine can be designed for oral or parenteral (including subcutaneous, intramuscular, intravenous, and intradermal) administration. When being administered orally, a pharmaceutical composition containing pentamidine can be in the form of a pill, tablet, or capsule. Compositions suitable for parenteral administration include aqueous and non-aqueous sterile injection solutions that can contain anti-oxidants, buffers, bacteriostats, and solutes that render the formulation isotonic with the blood of the intended recipient; and aqueous and non-aqueous sterile suspensions that may include suspending agents and thickening agents. The formulations can be presented in unit-dose or multi-dose containers, for example, sealed ampules and vials, and may be stored in a freeze dried (lyophilized) condition requiring only the addition of the sterile liquid carrier, for example water for injections, immediately prior to use. Extemporaneous injection solutions and suspensions may be prepared from sterile powders, granules, and tablets.

Such injection solutions can be in the form, for example, of a sterile injectable aqueous or oleaginous suspension. This suspension may be formulated using, for example, suitable dispersing or wetting agents (such as, for example, Tween 80) and suspending agents. The sterile injectable preparation can be a sterile injectable solution or suspension in a non-toxic parenterally-acceptable diluent or solvent, for example, as a solution in 1,3-butanediol. Examples of acceptable vehicles and solvents that can be used include, without limitation, mannitol, Ringer's solution, and isotonic sodium chloride solution. In addition, sterile, fixed oils can be used as a solvent or suspending medium. In some cases, a bland fixed oil can be used such as synthetic mono- or di-glycerides. Fatty acids, such as oleic acid and its glyceride derivatives can be used in the preparation of injectables, as can natural pharmaceutically-acceptable oils, such as olive oil or castor oil, including those in their polyoxyethylated versions. In some cases, these oil solutions or suspensions can contain a long-chain alcohol diluent or dispersant.

In some cases, a pharmaceutically acceptable composition including pentamidine can be administered locally or systemically. For example, a composition containing pentamidine can be administered locally by injection into lesions at surgery or by subcutaneous administration of a sustained release formulation. In some cases, a composition containing pentamidine can be administered systemically orally or by injection to a mammal (e.g., a human).

Effective doses can vary depending on the severity of the cancer, the route of administration, the age and general health condition of the subject, excipient usage, the possibility of co-usage with other therapeutic treatments such as use of chemotherapeutic agents, and the judgment of the treating physician.

An effective amount of a composition containing pentamidine can be any amount that reduces skin cancer progression without producing significant toxicity to the mammal. For example, an effective amount of pentamidine can be from about 0.01 mg/kg to about 4 mg/kg. In some cases, between about 10 mg and about 1500 mg of pentamidine can be administered to an average sized human (e.g., about 70-75 kg human) daily for about one week to about one year (e.g., about two weeks to about four months). If a particular mammal fails to respond to a particular amount, then the amount of pentamidine can be increased by, for example, two fold. After receiving this higher amount, the mammal can be monitored for both responsiveness to the treatment and toxicity symptoms, and adjustments made accordingly. The effective amount can remain constant or can be adjusted as a sliding scale or variable dose depending on the mammal's response to treatment. Various factors can influence the actual effective amount used for a particular application. For example, the frequency of administration, duration of treatment, use of multiple treatment agents, route of administration, and severity of the cancer may require an increase or decrease in the actual effective amount administered.

The frequency of administration can be any frequency that reduces skin cancer progression without producing significant toxicity to the mammal. For example, the frequency of administration can be from about once a week to about once every two to three weeks. The frequency of administration can remain constant or can be variable during the duration of treatment. A course of treatment with a composition containing pentamidine can include rest periods. For example, a composition containing pentamidine can be administered daily over a two week period followed by a two week rest period, and such a regimen can be repeated multiple times. As with the effective amount, various factors can influence the actual frequency of administration used for a particular application. For example, the effective amount, duration of treatment, use of multiple treatment agents, route of administration, and severity of the cancer may require an increase or decrease in administration frequency.

An effective duration for administering a composition containing pentamidine can be any duration that reduces skin cancer progression without producing significant toxicity to the mammal. Thus, the effective duration can vary from several days to several weeks, months, or years. In general, the effective duration for the treatment with pentamidine to reduce skin cancer progression can range in duration from six months to one year. Multiple factors can influence the actual effective duration used for a particular treatment. For example, an effective duration can vary with the frequency of administration, effective amount, use of multiple treatment agents, route of administration, and severity of the condition being treated.

In certain instances, a course of treatment and the severity of one or more symptoms related to the skin cancer being treated (e.g., pre-metastatic melanoma) can be monitored. Any appropriate method can be used to determine whether or not cancer progression is reduced. For example, the severity of a symptom of skin cancer can be assessed using imagine and pathology assessment of biopsy samples or surgical samples.

The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES Example 1—Marker Genes that Discriminate Between Benign and Malignant Tissue

Marker genes were ordered by their ability to differentiate benign from malignant tissue (Table A). This was based on the analysis of 73 benign and 53 malignant tissues, and the hypothesis that changes in expression of fibronectin-associated gene networks are indicative of malignant cell behavior. Values of the test statistic were for the Wilcoxon rank sum test. The values of the test statistic for a Winsorized two-sample test (trimmed outliers were replaced with actual values) and for the chi-square test for the zero vs. >zero versions of each variable were included. The top 5 discriminatory genes based on each statistical test were highlighted in bold.

TABLE A Test statistic value Wilcoxon Winsorized rank sum two-sample Chi- gene test t-test square test FN1 −10.2312 −8.04081 106.714 SPP1 −9.0279 −4.9374 86.774 COL4A1 −8.8807 −7.27171 83.711 TNC −8.7511 −8.31049 75.549 ITGA3 −8.6008 −5.86334 79.788 LOXL3 −8.1978 −6.75327 75.144 AGRN −8.1243 −7.91238 62.611 VCAN −8.0812 −6.24088 67.388 PLOD3 −8.0384 −6.89248 62.691 ITGB1 −8.0021 −7.38143 59.973 PTK2 −7.5279 −7.19889 54.446 CTGF −7.4997 −5.581 57.79 PLOD1 −7.332 −7.36126 44.87 LAMC1 −7.2425 −6.1057 54.233 THBS1 −7.2425 −5.60331 54.233 LOXL2 −7.2241 −6.33208 55.909 IL6 −7.1777 −6.41883 56.966 LOXL1 −7.1279 −6.34431 52.878 IL8 −7.1194 −5.76042 57.296 CYR61 −6.741 −6.97388 43.866 ITGAV −6.5947 −6.27571 47.021 YAP −6.4848 −6.36431 42.417 BGN −6.3419 −6.01066 25.387 LAMB1 −6.3293 −5.68826 37.061 ITGB3 −6.3142 −5.13158 40.835 CXCL1 −6.1077 −5.66564 40.137 THBS2 −6.0427 −5.02003 37.413 COL18A1 −6.0379 −4.9125 41.339 SPARC −6.0272 −6.39324 38.098 TP53 −6.0182 −6.18554 34.945 PLOD2 −5.9082 −3.50272 47.576 CCL2 −5.8844 −5.38758 30.69 FBLN2 −5.5848 −4.59826 31.913 LAMA1 −5.4876 −4.2817 31.071 THBS4 −5.3971 −3.88786 35.27 COL1A1 −5.325 −4.37617 34.693 ITGA5 −4.9847 −3.56695 25.243 TAZ −4.036 −3.26011 18.313 POSTN −3.8054 −2.78378 19.813 LOX −3.728 −2.8677 17.157 CSRC −3.7078 −3.71759 13.983 LAMA3 −3.5805 −2.99652 13.391 CDKN1A −3.5766 −3.20447 17.228 CDKN2A −3.5491 −2.90903 15.938 ITGA2 −3.4083 −2.72495 11.766 LAMC2 −3.4083 −2.53784 11.766 PCOLCE2 −3.3469 −3.53676 14.449 LOXL4 −3.2079 −2.76128 10.943 PCOLCE −2.2172 −1.13805 7.993 LAMB3 −1.2822 0.89459 7.028 CSF2 2.175 1.93095 4.522

Example 2—Marker Panel Revision after Statistical Analysis

The candidate gene list from Example 1 was modified to include other FN1 network genes as well as four housekeeping genes (ACTB, RPLP0, RPL8, and B2M), two keratinocyte markers (K10 and K14) to assess keratinocyte contamination, and four melanocyte markers (MITF, TYR, MLANA and PMEL) to assess melanocyte content in the skin sections. Genes from Example 1 with low discriminatory value and a more distant neighborhood to FN1 were excluded from the test setup (LAMC1, LOXL2, CYR61, YAP, BGN, LAMB1, THBS2, COL18A1, SPARC, TP53, PLOD2, CCL2, FBLN2, LAMA1, THBS4, COL1A1, TAZ, POSTN, LOX, CSRC, LAMA3, CDKN1A, CDKN2A, LAMC2, PCOLCE2, LOXL4, PCOLCE, LAMB3, and CSF2). Instead, the discriminatory ability of other FN1 network genes was determined (PLAT, CSK, GDF15, FARP1, ARPC1B, NES, NTRK3, SNX17, L1CAM, and CD44). The following results were based on the analysis of 26 benign nevi and 52 primary cutaneous melanomas with documented subsequent metastasis or skin lesions of melanoma metastasis (Table B). The top 5 genes were highlighted.

TABLE B Test Statistic value Wilcoxon Winsorized rank sum two-sample Chi-square gene test t-test test COL4A1 −5.85975 −5.42545 46.3273 FN1 −5.50862 −3.63639 35.1951 PLAT −4.82670 −3.13568 25.7234 IL8 −4.61443 −4.41668 28.6000 SPP1 −4.60153 −3.08137 23.0816 PLOD3 −4.37001 −3.91553 18.8036 TNC −4.26431 −3.14128 19.5000 CXCL1 −4.24452 −3.76681 20.6471 CSK −4.15178 −2.96444 18.3962 GDF15 −4.01364 −2.99752 13.7083 ITGB3 −3.92608 −2.80068 16.3091 CCL2 −3.61870 −3.45423 17.5176 VCAN −3.46906 −2.26781 12.5593 ITGB1 −3.40897 −3.63399 5.0221 PLOD1 −3.40380 −3.20309 9.2625 CTGF −3.11725 −2.20507 10.0645 THBS1 −3.11721 −2.01257 10.0645 ITGA3 −3.04915 −2.65398 7.5341 FARP1 −2.99724 −2.28024 9.2857 AGRN −2.92104 −3.30679 1.8838 IL6 −2.85960 −3.05600 10.6257 LOXL3 −2.84999 −2.70498 5.1096 LOXL1 −2.69957 −2.11477 8.1250 ARPC1B −2.57571 −2.82320 All but 1 value >0 NES −2.45264 −2.70056 2.4375 PTK2 −2.22328 −2.26180 4.4057 ITGA2 −2.08353 −1.50078 4.4571 ITGA5 −1.93478 −1.39663 3.8451 ITGAV −1.29341 −0.81964 3.5615 NTRK3 −1.22485 75 of the 78 values are = 0 MITF 0.58305 0.73916 0.4274 SNX17 0.74754 0.90733 0.0785 L1CAM 1.61125 0.27151 2.1081 MLANA 2.96258 2.92548 All values >0 CD44 5.23089 7.17590 All but 1 value >0

Based on the results of Example 1 and above, FN1 was identified as a component of the melanoma phenotype that is at the core of a gene network that discriminates between benign and malignant melanocytic skin lesions (FIG. 7). The modeling was based on the STRING 9.0 database (string-db.org).

The list of all 71 genes tested is provided in Table 1.

TABLE 1 List of genes used to discriminate benign skin tissue lesions from malignant skin tissue lesions. GenBank ® GenBank ® Gene Name Accession No. GI No. FN1 NM_212482 47132556 NM_002026 47132558 NM_212474 47132548 NM_212476 47132552 NM_212478 47132554 NM_054034 47132546 SPP1 NM_001040058 91206461 NM_001040060 91598938 NM_000582 38146097 COL4A1 NM_001845 148536824 TNC NM_002160 340745336 ITGA3 NM_005501 171846264 NM_002204 171846266 LOXL3 NM_032603 22095373 AGRN NM_198576 344179122 VCAN NM_004385 255918074 NM_001164098 255918078 NM_001164097 255918076 PLOD3 NM_001084 62739167 ITGB1 NM_002211 182519230 NM_133376 182507162 NM_033668 182507160 PTK2 NM_001199649 313851043 NM_005607 313851042 NM_153831 313851041 CTGF NM_001901 98986335 PLOD1 NM_000302 324710986 LAMC1 NM_002293 145309325 THBS1 NM_003246 40317625 LOXL2 NM_002318 67782347 IL6 NM_000600 224831235 LOXL1 NM_005576 67782345 IL8 NM_000584 324073503 CYR61 NM_001554 197313774 ITGAV NM_001144999 223468594 NM_001145000 223468596 NM_002210 223468593 YAP NM_001130145 303523503 NM_001195045 303523626 NM_006106 303523510 NM_001195044 303523609 BGN NM_001711 268607602 LAMB1 NM_002291 167614503 ITGB3 NM_000212 47078291 CXCL1 NM_001511 373432598 THBS2 NM_003247 40317627 COL18A1 NM_030582 110611234 NM_130445 110611232 SPARC NM_003118 365777426 TP53 NM_000546 371502114 NM_001126112 371502115 NM_001126114 371502117 NM_001126113 371502116 PLOD2 NM_182943 62739164 NM_000935 62739165 CCL2 NM_002982 56119169 FBLN2 NM_001998 51873054 NM_001004019 51873052 NM_001165035 259013546 LAMA1 NM_005559 329112585 THBS4 NM_003248 291167798 COL1A1 NM_000088 110349771 ITGA5 NM_002205 56237028 TAZ NM_000116 195232764 NM_181311 195232766 NM_181312 195232765 NM_181313 195232767 POSTN NM_001135934 209862910 NM_006475 209862906 NM_001135935 209863010 LOX NM_001178102 296010939 NM_002317 296010938 CSRC NM_005417 38202215 NM_198291 38202216 LAMA3 NM_198129 38045909 NM_001127717 189217424 CDKN1A NM_000389 310832422 NM_001220777 334085239 NM_078467 310832423 NM_001220778 334085241 CDKN2A NM_000077 300863097 NM_058195 300863095 NM_001195132 304376271 ITGA2 NM_002203 116295257 LAMC2 NM_005562 157419137 NM_018891 157419139 PCOLCE2 NM_013363 296317252 LOXL4 NM_032211 67782348 PCOLCE NM_002593 157653328 LAMB3 NM_000228 62868214 NM_001017402 62868216 NM_001127641 189083718 CSF2 NM_000758 371502128 ACTB NM_001101 168480144 RPLP0 NM_053275 49087137 NM_001002 49087144 RPL8 NM_000973 72377361 NM_033301 15431305 B2M NM_004048 37704380 K10 NM_000421 195972865 K14 NM_000526 197313720 MITF NM_198158 296841082 NM_198177 296841080 NM_006722 296841079 NM_198159 296841078 NM_000248 296841081 NM_001184967 296841084 NM_198178 296923803 TYR NM_000372 113722118 MLANA NM_005511 5031912 PMEL NM_001200054 318037594 NM_001200053 318037592 NM_006928 318068057 NES NM_006617 38176299 L1CAM NM_024003 221316758 NM_001143963 221316759 NM_000425 221316755 GDF15 NM_004864 153792494 ARPC1B NM_005720 325197176 FARP1 NM_005766 48928036 NM_001001715 159032536 NTRK3 NM_001007156 340745351 NM_001012338 340745349 NM_001243101 340745352 NM_002530 340745350 CSK NM_001127190 187475372 NM_004383 187475371 CD44 NM_001001391 48255940 NM_001001392 48255942 NM_001202556 321400139 NM_001001389 48255936 NM_000610 48255934 NM_001001390 48255938 NM_001202555 321400137 NM_001202557 321400141 SNX17 NM_014748 388596703 PLAT NM_000930 132626665 NM_033011 132626641

Gene expression of target genes was assessed by SYBR/EVA-Green based RT-PCR. All tested genes were accompanied by a standard curve for quantification of absolute copy number per a defined number of housekeeping genes. mRNA extraction from paraffin-embedded biospecimen was performed using an extraction protocol (Qiagen RNA FFPE extraction kit) and an extraction robot (Qiacube from Qiagen). mRNA was transcribed into cDNA using a commercially available kit (iScript kit from BioRad), and Fluidigm technology was used for PCR cycling.

The primer design was performed using web-based open access software. The primers were HPLC purified to minimize background and were optimized for formalin-fixed, paraffin-embedded (FFPE) tissue (i.e., highly degraded tissue). The primers were designed to detect a maximum number of gene transcripts and were designed to be cDNA specific (i.e., not affected by genomic DNA contamination of the total, tissue-derived cDNA). The housekeeping genes, keratin genes, melanocyte-specific genes, and selected high interest genes were detected using four separate and individually designed primer pairs. The primer pairs are set forth in Table 2.

TABLE 2 Primer sets for indicated genes. Gene Name Forward primer Reverse primer ACTB 5′-GCCAACCGCGAGAAGATG-3′; SEQ ID 5′-GGCTGGGGTGTTGAAGGT-3′; SEQ NO: 1 ID NO: 2 5′-CGCGAGAAGATGACCCAGAT-3′; SEQ 5′-GGGGTGTTGAAGGTCTCAAA-3′; ID NO: 3 SEQ ID NO: 4 5′-TGACCCAGATCATGTTTGAGA-3′; 5′-GTACATGGCTGGGGTGTTG-3′; SEQ SEQ ID NO: 5 ID NO: 6 5′-CTGAACCCCAAGGCCAAC-3′; SEQ ID 5′-TGATCTGGGTCATCTTCTCG-3′; NO: 7 SEQ ID NO: 8 RPLP0 5′-AACTCTGCATTCTCGCTTCC-3′; SEQ 5′-GCAGACAGACACTGGCAACA-3′; ID NO: 9 SEQ ID NO: 10 5′-GCACCATTGAAATCCTGAGTG-3′; 5′-GCTCCCACTTTGTCTCCAGT-3′; SEQ ID NO: 11 SEQ ID NO: 12 5′-TCACAGAGGAAACTCTGCATTC-3′; 5′-GGACACCCTCCAGGAAGC-3′; SEQ SEQ ID NO: 13 ID NO: 14 5′-ATCTCCAGGGGCACCATT-3′; SEQ ID 5′-AGCTGCACATCACTCAGGATT-3′; NO: 15 SEQ ID NO: 16 RPL8 5′-ACTGCTGGCCACGAGTACG-3′; SEQ 5′-ATGCTCCACAGGATTCATGG-3′; ID NO: 17 SEQ ID NO: 18 5′-ACAGAGCTGTGGTTGGTGTG-3′; SEQ 5′-TTGTCAATTCGGCCACCT-3′; SEQ ID NO: 19 ID NO: 20 5′-TATCTCCTCAGCCAACAGAGC-3′; 5′-AGCCACCACACCAACCAC-3′;SEQ SEQ ID NO: 21 ID NO: 22 5′-GTGTGGCCATGAATCCTGT-3′; SEQ ID 5′-CCACCTCCAAAAGGATGCTC-3′; NO: 23 SEQ ID NO: 24 B2M 5′-TCTCTCTTTCTGGCCTGGAG-3′; SEQ 5′-GAATCTTTGGAGTACGCTGGA-3′; ID NO: 25 SEQ ID NO: 26 5′-TGGAGGCTATCCAGCGTACT-3′; SEQ 5′-CGTGAGTAAACCTGAATCTTTGG-3′; ID NO: 27 SEQ ID NO: 28 5′-CCAGCGTACTCCAAAGATTCA-3′; 5′-TCTCTGCTGGATGACGTGAG-3′; SEQ ID NO: 29 SEQ ID NO: 30 5′-GGCTATCCAGCGTACTCCAA-3′; SEQ 5′-GCTGGATGACGTGAGTAAACC-3′; ID NO: 31 SEQ ID NO: 32 KRT14 5′-ACCATTGAGGACCTGAGGAA-3′; SEQ 5′-GTCCACTGTGGCTGTGAGAA-3′; ID NO: 33 SEQ ID NO: 34 5′-CATTGAGGACCTGAGGAACA-3′; SEQ 5′-AATCTGCAGAAGGACATTGG-3′; ID NO: 35 SEQ ID NO: 36 5′-GATGACTTCCGCACCAAGTA-3′; SEQ 5′-CGCAGGTTCAACTCTGTCTC-3′; ID NO: 37 SEQ ID NO: 38 5′-TCCGCACCAAGTATGAGACA-3′; SEQ 5′-ACTCATGCGCAGGTTCAACT-3′; ID NO: 39 SEQ ID NO: 40 KRT10 5′-GAGCCTCGTGACTACAGCAA-3′; SEQ 5′-GCAGGATGTTGGCATTATCAGT-3′; ID NO: 41 SEQ ID NO: 42 5′-AAAACCATCGATGACCTTAAAAA-3′; 5′-GATCTGAAGCAGGATGTTGG-3′; SEQ ID NO: 43 SEQ ID NO: 44 MITF 5′-TTCCCAAGTCAAATGATCCAG-3′; 5′-AAGATGGTTCCCTTGTTCCA-3′; SEQ ID NO: 45 SEQ ID NO: 46 5′-CGGCATTTGTTGCTCAGAAT-3′; SEQ 5′-GAGCCTGCATTTCAAGTTCC-3′; ID NO: 47 SEQ ID NO: 48 TYR 5′-TTCCTTCTTCACCATGCATTT-3′; SEQ 5′-GGAGCCACTGCTCAAAAATA-3′; ID NO: 49 SEQ ID NO: 50 5′-TCCAAAGATCTGGGCTATGA-3′; SEQ 5′-TTGAAAAGAGTCTGGGTCTGAA-3′; ID NO: 51 SEQ ID NO: 52 MLANA 5′-GAGAAAAACTGTGAACCTGTGG-3′; 5′-ATAAGCAGGTGGAGCATTGG-3′; SEQ ID NO: 53 SEQ ID NO: 54 5′-GAAGACGAAATGGATACAGAGC-3′; 5′-GTGCCAACATGAAGACTTTTATC-3′; SEQ ID NO: 55 SEQ ID NO: 56 PMEL 5′-GTGGTCAGCACCCAGCTTAT-3′; SEQ 5′-CCAAGGCCTGCTTCTTGAC-3′; SEQ ID NO: 57 ID NO: 58 5′-GCTGTGGTCCTTGCATCTCT-3′; SEQ 5′-GCTTCATAAGTCTGCGCCTA-3′; ID NO: 59 SEQ ID NO: 60 FN1 5′-CTCCTGCACATGCTTTGGA-3′; SEQ ID 5′-AGGTCTGCGGCAGTTGTC-3′; SEQ NO: 61 ID NO: 62 5′-AGGCTTTGGAAGTGGTCATT-3′; SEQ 5′-CCATTGTCATGGCACCATCT-3′; ID NO: 63 SEQ ID NO: 64 5′-GAAGTGGTCATTTCAGATGTGATT-3′; 5′-CCATTGTCATGGCACCATCT-3′; SEQ ID NO: 65 SEQ ID NO: 66 5′-TGGTCATTTCAGATGTGATTCAT-3′; 5′-CATTGTCATGGCACCATCTA-3′; SEQ ID NO: 67 SEQ ID NO: 68 SPP1 5′-GTTTCGCAGACCTGACATCC-3′; SEQ 5′-TCCTCGTCTGTAGCATCAGG-3′; ID NO: 69 SEQ ID NO: 70 5′-CCTGACATCCAGTACCCTGA-3′; SEQ 5′-TGAGGTGATGTCCTCGTCTG-3′; ID NO: 71 SEQ ID NO: 72 5′-GAATCTCCTAGCCCCACAGA-3′; SEQ 5′-GGTTTCTTCAGAGGACACAGC-3′; ID NO: 73 SEQ ID NO: 74 5′-CCCATCTCAGAAGCAGAATCTC-3′; 5′-ACAGCATTCTGTGGGGCTA-3′; SEQ SEQ ID NO: 75 ID NO: 76 COL4A1 5′-GGAAAACCAGGACCCAGAG-3′; SEQ 5′-CTTTTTCCCCTTTGTCACCA-3′; SEQ ID NO: 77 ID NO: 78 5′-AGAAAGGTGAACCCGGAAAA-3′; 5′-GGTTTGCCTCTGGGTCCT-3′; SEQ SEQ ID NO: 79 ID NO: 80 5′-GAGAAAAGGGCCAAAAAGGT-3′; 5′-CATCCCCTGAAATCCAGGTT-3′; SEQ ID NO: 81 SEQ ID NO: 82 5′-AAAGGGCCAAAAAGGTGAAC-3′; 5′-CCTGGCATCCCCTGAAAT-3′; SEQ SEQ ID NO: 83 ID NO: 84 TNC 5′-GTGTCAACCTGATGGGGAGA-3′; SEQ 5′-GTTAACGCCCTGACTGTGGT-3′; ID NO: 85 SEQ ID NO: 86 5′-GGTACAGTGGGACAGCAGGT-3′; SEQ 5′-GATCTGCCATTGTGGTAGGC-3′; ID NO: 87 SEQ ID NO  88 5′-AACCACAGTCAGGGCGTTA-3′; SEQ 5′-GTTCGTGGCCCTTCCAGT-3′; SEQ ID NO: 89 ID NO: 90 5′-AAGCTGAAGGTGGAGGGGTA-3′; SEQ 5′-GAGTCACCTGCTGTCCCACT-3′; ID NO: 91 SEQ ID NO: 92 ITGA3 5′-TATTCCTCCGAACCAGCATC-3′; SEQ 5 -CACCAGCTCCGAGTCAATGT-3′; ID NO: 93 SEQ ID NO: 94 5′-CCACCATCAACATGGAGAAC-3′; SEQ 5′-AGTCAATGTCCACAGAGAACCA-3′; ID NO: 95 SEQ ID NO: 96 LOXL3 5′-CAACTGCCACATTGGTGATG-3′; SEQ 5′-AAACCTCCTGTTGGCCTCTT-3′; ID NO: 97 SEQ ID NO: 98 5′-TGACATCACGGATGTGAAGC-3′; SEQ 5′-GGGTTGATGACAACCTGGAG-3′; ID NO: 99 SEQ ID NO: 100 AGRN 5′-TGTGACCGAGAGCGAGAAG-3′; SEQ 5′-CAGGCTCAGTTCAAAGTGGTT-3′; ID NO: 101 SEQ ID NO: 102 5′-CGGACCTTTGTCGAGTACCT-3′; SEQ 5′-GTTGCTCTGCAGTGCCTTCT-3′; ID NO: 103 SEQ ID NO: 104 VCAN 5′-GACTTCCGTTGGACTGATGG-3′; SEQ 5′-TGGTTGGGTCTCCAATTCTC-3′; ID NO: 105 SEQ ID NO: 106 5′-ACGTGCAAGAAAGGAACAGT-3′; SEQ 5′-TCCAAAGGTCTTGGCATTTT-3′; ID NO: 107 SEQ ID NO: 108 PLOD3 5′-GCAGAGATGGAGCACTACGG-3′; SEQ 5′-CAGCCTTGAATCCTCATGC-3′; SEQ ID NO: 109 ID NO: 110 5′-GGAAGGAATCGTGGAGCAG-3′; SEQ 5′-CAGCAGTGGGAACCAGTACA-3′; ID NO: 111 SEQ ID NO: 112 ITGB1 5′-CTGATGAATGAAATGAGGAGGA-3′; 5′-CACAAATGAGCCAAATCCAA-3′; SEQ ID NO: 113 SEQ ID NO: 114 5′-CAGTTTGCTGTGTGTTTGCTC-3′; SEQ 5′-CATGATTTGGCATTTGCTTTT-3′; ID NO: 115 SEQ ID NO: 116 PTK2 5′-GCCCCACCAGAGGAGTATGT-3′; SEQ 5′-AAGCCGACTTCCTTCACCA-3′; SEQ ID NO: 117 ID NO: 118 5′-GAGACCATTCCCCTCCTACC-3′; SEQ 5′-GCTTCTGTGCCATCTCAATCT-3′; ID NO: 119 SEQ ID NO: 120 CTGF 5′-CGAAGCTGACCTGGAAGAGA-3′; SEQ 5′-TGGGAGTACGGATGCACTTT-3′; ID NO: 121 SEQ ID NO: 122 5′-GTGTGCACCGCCAAAGAT-3′; SEQ ID 5′-CGTACCACCGAAGATGCAG-3′; NO: 123 SEQ ID NO: 124 PLOD1 5′-CTACCCCGGCTACTACACCA-3′; SEQ 5′-GACAAAGGCCAGGTCAAACT-3′; ID NO: 125 SEQ ID NO: 126 5′-AGTCGGGGTGGATTACGAG-3′; SEQ 5′-ACAGTTGTAGCGCAGGAACC-3′; ID NO: 127 SEQ ID NO: 128 LAMC1 5′-ATGATGATGGCAGGGATGG-3′; SEQ 5′-GCATTGATCTCGGCTTCTTG-3′; ID NO: 129 SEQ ID NO: 130 THBS1 5′-CTGTGGCACACAGGAAACAC-3′; SEQ 5′-ACGAGGGTCATGCCACAG-3′; SEQ ID NO: 131 ID NO: 132 5′-GCCAAAGACGGGTTTCATTA-3′; SEQ 5′-GCCATGATTTTCTTCCCTTC-3′; ID NO: 133 SEQ ID NO: 134 LOXL2 5′-CTCCTCCTACGGCAAGGGA-3′; SEQ 5′-TGGAGATTGTCTAACCAGATGGG-3′; ID NO: 135 SEQ ID NO: 136 5′-CTCCTACGGCAAGGGAGAAG-3′; SEQ 5′-TTGCCAGTACAGTGGAGATTG-3′; ID NO: 137 SEQ ID NO: 138 IL6 5′-CCAGAGCTGTGCAGATGAGT-3′; SEQ 5′-TGCATCTAGATTCTTTGCCTTTT-3′; ID NO: 139 SEQ ID NO: 140 LOXL1 5′-AGGGCACAGCAGACTTCCT-3′; SEQ 5′-TCGTCCATGCTGTGGTAATG-3′; ID NO:  141 SEQ ID NO: 142 5′-GCATGCACCTCTCATACCC-3′; SEQ ID 5′-CGCATTGTAGGTGTCATAGCA-3′; NO: 143 SEQ ID NO: 144 IL8 5′-GCAAAACTGCACCTTCACAC-3′; 5′-CTTGGCAGCCTTCCTGATT-3′; SEQ ID NO: 145 SEQ ID NO: 146 CYR61 5′-CGCTCTGAAGGGGATCTG-3′; SEQ ID 5′-ACAGGGTCTGCCCTCTGACT-3′; NO: 147 SEQ ID NO: 148 5′-GAGCTCAGTCAGAGGGCAGA-3′; SEQ 5′-AACTTTCCCCGTTTTGGTAGA-3′; ID NO: 149 SEQ ID NO: 150 ITGAV 5′-GACCTTGGAAACCCAATGAA-3′; SEQ 5′-TCCATCTCTGACTGCTGGTG-3′; ID NO: 431 SEQ ID NO: 432 5′-GGTGGTATGTGACCTTGGAAA-3′; 5′-GCACACTGAAACGAAGACCA-3′; SEQ ID NO: 439 SEQ ID NO: 440 YAP 5′-TGAACAGTGTGGATGAGATGG-3′; 5′-GCAGGGTGCTTTGGTTGATA-3′; SEQ ID NO: 151 SEQ ID NO: 152 BGN 5′-AAGGGTCTCCAGCACCTCTAC-3′; 5′-AAGGCCTTCTCATGGATCTT-3′; SEQ ID NO: 153 SEQ ID NO: 154 5′-GAGCTCCGCAAGGATGACT-3′; SEQ 5′-AGGACGAGGGCGTAGAGGT-3′; ID NO: 155 SEQ ID NO: 156 LAMB1 5′-CATTCAAGGAACCCAGAACC-3′; SEQ 5′-GCGTTGAACAAGGTTTCCTC-3′; ID NO: 157 SEQ ID NO: 158 ITGB3 5′-AAGAGCCAGAGTGTCCCAAG-3′; SEQ 5′-ACTGAGAGCAGGACCACCA-3′; ID NO: 159 SEQ ID NO: 160 5′-CTTCTCCTGTGTCCGCTACAA-3′; SEQ 5′-CATGGCCTGAGCACATCTC-3′; SEQ ID NO: 161 ID NO: 162 5′-TGCCTGCACCTTTAAGAAAGA-3′; 5′-CCGGTCAAACTTCTTACACTCC-3′; SEQ ID NO: 163 SEQ ID NO: 164 5′-AAGGGGGAGATGTGCTCAG-3′; SEQ 5′-CAGTCCCCACAGCTGCAC-3′; SEQ ID NO: 165 ID NO: 166 CXCL1 5′-AAACCGAAGTCATAGCCACAC-3′; 5′-AAGCTTTCCGCCCATTCTT-3′; SEQ SEQ ID NO: 167 ID NO: 168 THBS2 5′-AGGCCCAAGACTGGCTACAT-3′; SEQ 5′-CTGCCATGACCTGTTTTCCT-3′; ID NO: 169 SEQ ID NO: 170 5′-GGCAGGTGCGAACCTTATG-3′; SEQ 5′-CCTTCCAGCCAATGTTCCT-3′; SEQ ID NO: 171 ID NO: 172 COL18A1 5′-GATCGCTGAGCTGAAGGTG-3′; SEQ 5′-CGGATGCCCCATCTGAGT-3′; SEQ ID NO: 173 ID NO: 174 SPARC 5′-CCCATTGGCGAGTTTGAGAAG-3′; 5′-AGGAAGAGTCGAAGGTCTTGTT-3′; SEQ ID NO: 175 SEQ ID NO: 176 5′-GGAAGAAACTGTGGCAGAGG-3′; SEQ 5′-GGACAGGATTAGCTCCCACA-3′; ID NO: 177 SEQ ID NO: 178 TP53 5′-ACAACGTTCTGTCCCCCTTG-3′; SEQ 5′-GGGGACAGCATCAAATCATC-3′; ID NO: 179 SEQ ID NO: 180 PLOD2 5′-TGGATGCAGATGTTGTTTTGA-3′; SEQ 5′-CACAGCTTTCCATGACGAGTT-3′; ID NO: 181 SEQ ID NO: 182 5′-TTGATTGAACAAAACAGAAAGATCA-3′; 5′-TGACGAGTTACAAGAGGAGCAA-3′; SEQ ID NO: 183 SEQ ID NO: 184 CCL2 5′-CTGCTCATAGCAGCCACCTT-3′; SEQ 5′-AGGTGACTGGGGCATTGATT-3′; ID NO: 185 SEQ ID NO: 186 FBLN2 5′-ACGTGGAGGAGGACACAGAC-3′; SEQ 5′-GGAGCCTTCAGGGCTACTTC-3′; ID NO: 187 SEQ ID NO: 188 LAMA1 5′-AGCACTGCCAAAGTGGATG-3′; SEQ 5′-TTGTTGACATGGAACAAGACC-3′; ID NO: 189 SEQ ID NO: 190 THBS4 5′-GTGGGCTACATCAGGGTACG-3′; SEQ 5′-CAGAGTCAGCCACCAACTCA-3′; ID NO: 191 SEQ ID NO: 192 5′-CATCATCTGGTCCAACCTCA-3′; SEQ 5′-GTCCTCAGGGATGGTGTCAT-3′; ID NO: 193 SEQ ID NO: 194 COL1A1 5′-TGACCTCAAGATGTGCCACT-3′; SEQ 5′-TGGTTGGGGTCAATCCAGTA-3′; ID NO: 195 SEQ ID NO: 196 5′-GATGGATTCCAGTTCGAGTATG-3′; 5′-ATCAGGCGCAGGAAGGTC-3′; SEQ SEQ ID NO: 197 ID NO: 198 ITGAS 5′-CCCAAAAAGAGCGTCAGGT-3′; SEQ 5′-TTGTTGACATGGAACAAGACC-3′; ID NO: 199 SEQ ID NO: 200 TAZ 5′-CTTCCTAACAGTCCGCCCTA-3′; SEQ 5′-CCCGATCAGCACAGTGATTT-3′; ID NO: 201 SEQ ID NO: 202 POSTN 5′-CTGCTTCAGGGAGACACACC-3′; SEQ 5′-TGGCTTGCAACTTCCTCAC-3′; SEQ ID NO: 203 ID NO: 204 5′-AGGAAGTTGCAAGCCAACAA-3′; SEQ 5′-CGACCTTCCCTTAATCGTCTT-3′; ID NO: 205 SEQ ID NO: 206 LOX 5′-GCGGAGGAAAACTGTCTGG-3′; SEQ 5′-AAATCTGAGCAGCACCCTGT-3′; ID NO: 207 SEQ ID NO: 208 5′-ATATTCCTGGGAATGGCACA-3′; SEQ 5′-CCATACTGTGGTAATGTTGATGA-3′; ID NO: 209 SEQ ID NO: 210 CSRC 5′-TGTCAACAACACAGAGGGAGA-3′; 5′ -CACGTAGTTGCTGGGGATGT-3′; SEQ ID NO: 211 SEQ ID NO: 212 5′-TGGCAAGATCACCAGACGG-3′; SEQ 5′-GGCACCTTTCGTGGTCTCAC-3′; ID NO: 213 SEQ ID NO: 214 LAMA3 5′-CATGTCGTCTTGGCTCACTC-3′; SEQ 5′-AAATTCTGGCCCCAACAATAC-3′; ID NO: 215 SEQ ID NO: 216 CDKN1A 5′-CATGTCGTCTTGGCTCACTC-3′; SEQ 5′-AAATTCTGGCCCCAACAATAC-3′; ID NO: 217 SEQ ID NO: 218 CDKN2A 5′-AGGAGCCAGCGTCTAGGG-3′; SEQ ID 5′-CTGCCCATCATCATGACCT-3′; SEQ NO: 219 ID NO: 220 5′-AACGCACCGAATAGTTACGG-3′; SEQ 5′-CATCATCATGACCTGGATCG-3′; ID NO: 221 SEQ ID NO: 222 ITGA2 5′-CACTGTTACGATTCCCCTGA-3′; SEQ 5′-CGGCTTTCTCATCAGGTTTC-3′; ID NO: 223 SEQ ID NO: 224 LAMC2 5′-ATTAGACGGCCTCCTGCATC-3′; SEQ 5′-AGACCAGCCCCTCTTCATCT-3′; ID NO: 225 SEQ ID NO: 226 PCOLCE2 5′-TACTTGGAAAATCACAGTTCCCG-3′; 5′-TGAATCGGAAATTGAGAACGACT-3′; SEQ ID NO: 443 SEQ ID NO: 444 LOXL4 5′-GGCCCCGGGAATTATATCT-3′; SEQ 5′-CCACTTCATAGTGGGGGTTC-3′; ID NO: 227 SEQ ID NO: 228 5′-CTGCACAACTGCCACACAG-3′; SEQ 5′-GTTCTGCATTGGCTGGGTAT-3′; ID NO: 229 SEQ ID NO: 230 PCOLCE 5′-CGTGGCAAGTGAGGGGTTC-3′; SEQ 5′-CGAAGACTCGGAATGAGAGGG-3′; ID NO: 231 SEQ ID NO: 232 5′-GAGGCTTCCTGCTCTGGT-3′; SEQ ID 5′-CGCAAAATTGGTGCTCAGT-3′; SEQ NO: 233 ID NO: 234 LAMB3 5′-GTCCGGGACTTCCTAACAGA-3′; SEQ 5′-GCTGACCTCCTGGATAGTGG-3′; ID NO:  235 SEQ ID NO:  236 PMEL 5′-GTGGTCAGCACCCAGCTTAT-3′; SEQ 5′-CCAAGGCCTGCTTCTTGAC-3′; SEQ ID NO: 237 ID NO: 238 5′-GCTGTGGTCCTTGCATCTCT-3′; SEQ 5′-GCTTCATAAGTCTGCGCCTA-3′; ID NO: 239 SEQ ID NO: 240 NES 5′-CTTCCCTCAGCTTTCAGGAC-3′; SEQ 5′-TCTGGGGTCCTAGGGAATTG-3′; ID NO: 241 SEQ ID NO: 242 5′-ACCTCAAGATGTCCCTCAGC-3′; SEQ 5′-CAGGAGGGTCCTGTACGTG-3′; ID NO: 243 SEQ ID NO: 244 L1CAM 5′-GAGACCTTCGGCGAGTACAG-3′; SEQ 5′-AAAGGCCTTCTCCTCGTTGT-3′; ID NO: 245 SEQ ID NO: 246 5′-GGCGGCAAATACTCAGTGAA-3′; SEQ 5′-CCTGGGTGTCCTCCTTATCC-3′; ID NO: 247 SEQ ID NO: 248 GDF15 5′-CGGATACTCACGCCAGAAGT-3′; SEQ 5′-AGAGATACGCAGGTGCAGGT-3′; ID NO: 249 SEQ ID NO: 250 5′-AAGATTCGAACACCGACCTC-3′; SEQ 5′-GCACTTCTGGCGTGAGTATC-3′; ID NO: 251 SEQ ID NO: 252 ARPC1B 5′-CACGCCTGGAACAAGGAC-3′; SEQ ID 5′-ATGCACCTCATGGTTGTTGG-3′; NO: 253 SEQ ID NO: 254 5′-CAGGTGACAGGCATCGACT-3′; SEQ 5′-CGCAGGTCACAATACGGTTA-3′; ID NO: 255 SEQ ID NO: 256 FARP1 5′-TGAGGCCCTGAGAGAGAAGA-3′; SEQ 5′-ATTCCGAAACTCCACACGTC-3′; ID NO: 257 SEQ ID NO: 258 5′-TCAAGGAAATTGAGCAACGA-3′; SEQ 5′-TCTGATTTGGGCATTTGAGC-3′; ID NO: 259 SEQ ID NO: 260 NTRK3 5′-TATGGTCGACGGTCCAAAT-3′; SEQ 5′-TCCTCACCACTGATGACAGC-3′; ID NO: 261 SEQ ID NO: 262 5′-CACTGTGACCCACAAACCAG-3′; SEQ 5′-GCAAGTCCAACTGCTATGGA-3′; ID NO: 263 SEQ ID NO: 264 CSK 5′-TGAGGCCCTGAGAGAGAAGA-3′; SEQ 5′-ATTCCGAAACTCCACACGTC-3′; ID NO: 265 SEQ ID NO: 266 5′-TCTACTCCTTTGGGCGAGTG-3′; SEQ 5′-CGTCCTTCAGGGGAATTCTT-3′; ID NO: 267 SEQ ID NO: 268 CD44 5′-TAAGGACACCCCAAATTCCA-3′; SEQ 5′-GCCAAGATGATCAGCCATTC-3′; ID NO: 269 SEQ ID NO: 270 5′-GCAGTCAACAGTCGAAGAAGG-3′; 5′-AGCTTTTTCTTCTGCCCACA-3′; SEQ ID NO: 271 SEQ ID NO: 272 SNX17 5′-AGCCAGCAAGCAGTGAAGTC-3′; SEQ 5′-TCAGGTGACTCAAGCAGTGG-3′; ID NO: 273 SEQ ID NO: 274 5′-CCGGGAGTCTATGGTCAAAC-3′; SEQ 5′-CACGGCACTCAGCTTACTTG-3′; ID NO: 275 SEQ ID NO: 276 PLAT 5′-TGGAGCAGTCTTCGTTTCG-3′; SEQ ID 5′-CTGGCTCCTCTTCTGAATCG-3′; NO: 277 SEQ ID NO: 278 5′-GCCCGATTCAGAAGAGGAG-3′; SEQ 5′-TCATCTCTGCAGATCACTTGG-3′; ID NO: 279 SEQ ID NO: 280

The following was performed to generate a standard curve for the target of each primer pair. The standard was generated with a defined number of amplicons per volume for each primer pair. In particular, a standard (S7) was designed to contain about 5 million copies of amplicon-containing cDNA in a bacterial expression vector backbone (pJET1.2 obtained from Fermentas) per one microliter volume for each primer pair. From this, six 1:10 dilutions were generated such that seven standards S1 to S7 were obtained ranging from 5 to 5 million copies of amplicon. To obtain fragments of cDNA, total RNA was extracted from the human HaCaT, A431, and A375 cell lines, and the RNA was reverse transcribed into cDNA. Cell line-derived cDNA was used as a template to amplify fragments of cDNA that contained the desired amplicons for the real time-PCR primer pairs. A list of primers used to generate the desired cDNA fragments is listed in Table 3.

TABLE 3 Primer sets for generating cDNA fragments of the indicated genes. Gene Name Forward primer Reverse primer FN1 5′-CCAGCAGAGGCATAAGGTTC-3′; SEQ ID 5′-AGTAGTGCCTTCGGGACTGG-3′; SEQ ID NO: 281 NO: 282 SPP1 5′-AGGCTGATTCTGGAAGTTCTGAGG-3′; SEQ 5′-AATCTGGACTGCTTGTGGCTG-3′; SEQ ID ID NO: 283 NO: 284 COL4A1 5′-GTTGGGCCTCCAGGATTTA-3′; SEQ ID 5′-GCCTGGTAGTCCTGGGAAAC-3′; SEQ ID NO: 285 NO: 286 TNC 5′-TGGATGGATTGTGTTCCTGA-3′; SEQ ID 5′-GCCTGCCTTCAAGATTTCTG-3′; SEQ ID NO: 287 NO: 288 ITGA3 5′-CTGAGACTGTGCTGACCTGTG-3′; SEQ ID 5′-CTCTTCATCTCCGCCTTCTG-3′; SEQ ID NO: 289 NO: 290 LOXL3 5′-GAGACCGCCTACATCGAAGA-3′; SEQ ID 5′-GGTAGCGTTCAAACCTCCTG-3′; SEQ ID NO: 291 NO: 292 AGRN 5′-ACACCGTCCTCAACCTGAAG-3′; SEQ ID 5′-AATGGCCAGTGCCACATAGT-3′; SEQ ID NO: 293 NO: 294 VCAN 5′-GGTGCACTTTGTGAGCAAGA-3′; SEQ ID 5′-TTGGTATGCAGATGGGTTCA-3′; SEQ ID NO: 295 NO: 296 PLOD3 5′-AGCTGTGGTCCAACTTCTGG-3′; SEQ ID 5′-GTGTGGTAACCGGGAAACAG-3′; SEQ ID NO: 297 NO: 298 ITGB1 5′-TTCAGTTTGCTGTGTGTTTGC-3′; SEQ ID 5′-CCACCTTCTGGAGAATCCAA-3′; SEQ ID NO: 299 NO: 300 PTK2 5′-GGCAGTATTGACAGGGAGGA-3′; SEQ ID 5′-TACTCTTGCTGGAGGCTGGT-3′; SEQ ID NO: 301 NO: 302 CTGF 5′-GCCTATTCTGTCACTTCGGCTC-3′; SEQ ID 5′-GCAGGCACAGGTCTTGATGAAC-3′; SEQ ID NO: 303 NO: 304 PLOD1 5′-GACCTCTGGGAGGTGTTCAG-3′; SEQ ID 5′-TTAGGGATCGACGAAGGAGA-3′; SEQ ID NO: 305 NO: 306 LAMC1 5′-ATTCCTGCCATCAACCAGAC-3′; SEQ ID 5′-CCTGCTTCTTGGCTTCATTC-3′; SEQ ID NO: 307 NO: 308 THBS1 5′-CAAAGGGACATCCCAAAATG-3′; SEQ ID 5′-GAGTCAGCCATGATTTTCTTCC-3′; SEQ ID NO: 309 NO: 310 LOXL2 5′-TACCCCGAGTACTTCCAGCA-3′; SEQ ID 5′-GATCTGCTTCCAGGTCTTGC-3′; SEQ ID NO: 311 NO: 312 IL6 5′-CACACAGACAGCCACTCACC-3′; SEQ ID 5′-CAGGGGTGGTTATTGCATCT-3′; SEQ ID NO: 313 NO: 314 LOXL1 5′-CAGACCCCAACTATGTGCAA-3′; SEQ ID 5′-CGCATTGTAGGTGTCATAGCA-3′; SEQ ID NO: 315 NO: 316 IL8 5′-CTCTCTTGGCAGCCTTCCT-3′; SEQ ID 5′-TGAATTCTCAGCCCTCTTCAA-3′; SEQ ID NO: 317 NO: 318 CYR61 5′-TCGCCTTAGTCGTCACCCTT-3′; SEQ ID 5′-TGTTTCTCGTCAACTCCACCTCG-3′; SEQ ID NO: 319 NO: 320 ITGAV 5′-CTGATTTCATCGGGGTTGTC-3′; SEQ ID 5′-TGCCTTGCTGAATGAACTTG-3′; SEQ ID NO: 321 NO: 322 YAP 5′-CCAGTGAAACAGCCACCAC-3′; SEQ ID 5′-CTCCTTCCAGTGTTCCAAGG-3′; SEQ ID NO: 323 NO: 324 BGN 5′-GGACTCTGTCACACCCACCT-3′; SEQ ID 5′-CAGGGTCTCAGGGAGGTCTT-3′; SEQ ID NO: 325 NO: 326 LAMB1 5′-TGCCAGAGCTGAGATGTTGTT-3′; SEQ ID 5′-TGTAGCATTTCGGCTTTCCT-3′; SEQ ID NO: 327 NO: 328 ITGB3 5′-GGCAAGTACTGCGAGTGTGA-3′; SEQ ID 5′-ATTCTTTTCGGTCGTGGATG-3′; SEQ ID NO: 329 NO: 330 CXCL1 5′-CACTGCTGCTCCTGCTCCT-3′; SEQ ID 5′-TGTTCAGCATCTTTTCGATGA-3′; SEQ ID NO: 331 NO: 332 THBS2 5′-TGACAATGACAACATCCCAGA-3′; SEQ ID 5′-TGAGTCTGCCATGACCTGTT-3′; SEQ ID NO: 333 NO: 334 COL18A1 5′-CCCTGCTCTACACAGAACCAG-3′; SEQ ID 5′-ACACCTGGCTCCCCTTTCT-3′; SEQ ID NO: 335 NO: 336 SPARC 5′-GCCTGGATCTTCTTTCTCCTTTGC-3′; SEQ 5′-CATCCAGGGCGATGTACTTGTC-3′; SEQ ID ID NO: 337 NO: 338 TP53 5′-CCCCCTCTGAGTCAGGAAAC-3′; SEQ ID 5′-TCATGTGCTGTGACTGCTTG-3′; SEQ ID NO: 339 NO: 340 PLOD2 5′-TGGACCCACCAAGATTCTCCTG-3′; SEQ 5′-GACCACAGCTTTCCATGACGAG-3′; SEQ ID ID NO: 341 NO: 342 CCL2 5′-TCTGTGCCTGCTGCTCATAG-3′; SEQ ID 5′-GAGTTTGGGTTTGCTTGTCC-3′; SEQ ID NO: 343 NO: 344 FBLN2 5′-CGAGAAGTGCCCAGGAAG-3′; SEQ ID 5′-AGTGAGAAGCCAGGAAAGCA-3′; SEQ ID NO: 345 NO: 346 LAMA1 5′-TGGAAATATCACCCACAGCA-3′; SEQ ID 5′-AGGCATTTTTGCTTCACACC-3′; SEQ ID NO: 347 NO: 348 THBS4 5′-GCTCCAGCTTCTACGTGGTC-3′; SEQ ID 5′-TTAATTATCGAAGCGGTCGAA-3′; SEQ ID NO: 349 NO: 350 COL1A1 5′-AGCCAGCAGATCGAGAACAT-3′; SEQ ID 5′-CCTTCTTGAGGTTGCCAGTC-3′; SEQ ID NO: 351 NO: 352 ITGA5 5′-CACCAATCACCCCATTAACC-3′; SEQ ID 5′-GCTTGAGCTGAGCTTTTTCC-3′; SEQ ID NO: 353 NO: 354 TAZ 5′-CCAGGTGCTGGAAAAAGAAG-3′; SEQ ID 5′-GAGCTGCTCTGCCTGAGTCT-3′; SEQ ID NO: 355 NO: 356 POSTN 5′-GCAGACACACCTGTTGGAAA-3′; SEQ ID 5′-GAACGACCTTCCCTTAATCG-3′; SEQ ID NO: 357 NO: 358 LOX 5 ′-CCTACTACATCCAGGCGTCCAC-3′; SEQ 5′-ATGCAAATCGCCTGTGGTAGC-3′; SEQ ID ID NO: 359 NO: 360 CSRC 5′-CTGTTCGGAGGCTTCAACTC-3′; SEQ ID 5′-AGGGATCTCCCAGGCATC-3′; SEQ ID NO: 361 NO: 362 LAMA3 5′-TACCTGGGATCACCTCCATC-3′; SEQ ID 5′-ACAGGGATCCTCAGTGTCGT-3′; SEQ ID NO: 363 NO: 364 CDKN1A 5′-CGGGATGAGTTGGGAGGAG-3′; SEQ ID 5′-TTAGGGCTTCCTCTTGGAGA-3′; SEQ ID NO: 365 NO: 366 CDKN2A- 5′-ATGGTGCGCAGGTTCTTG-3′; SEQ ID 5′-ACCAGCGTGTCCAGGAAG-3′; SEQ ID 004 2A-201 NO: 367 NO: 368 CDKN2A- 5′-GAGCAGCATGGAGCCTTC-3′; SEQ ID 5′-GCATGGTTACTGCCTCTGGT-3′; SEQ ID 001 2A-202 NO: 369 NO: 370 ITGA2 5′-CAAACAGACAAGGCTGGTGA-3′; SEQ ID 5′-TCAATCTCATCTGGATTTTTGG-3′; SEQ ID NO: 371 NO: 372 LAMC2 5′-CTGCAGGTGGACAACAGAAA-3′; SEQ ID 5′-CATCAGCCAGAATCCCATCT-3′; SEQ ID NO: 373 NO: 374 PCOLCE2 5′-GTCCCCAGAGAGACCTGTTT-3′; SEQ ID 5′-AGACACAATTGGCGCAGGT-3′; SEQ ID NO: 375 NO: 376 LOXL4 5′-AAGACTGGACGCGATAGCTG-3′; SEQ ID 5′-GGTTGTTCCTGAGACGCTGT-3′; SEQ ID NO: 377 NO: 378 PCOLCE 5′-TACACCAGACCCGTGTTCCT-3′ SEQ ID 5′-TCCAGGTCAAACTTCTCGAAGG-3′; SEQ ID NO: 379 NO: 380 LAMB3 5′-CTTCAATGCCCAGCTCCA-3′; SEQ ID 5′-TTCCCAACCACATCTTCCAC-3′; SEQ ID NO: 381 NO: 382 CSF2 5′-CTGCTGCTCTTGGGCACT-3′; SEQ ID 5′-CAGCAGTCAAAGGGGATGAC-3′; SEQ ID NO: 383 NO: 384 ACTB 5′-AGGATTCCTATGTGGGCGACG-3′; SEQ ID 5′-TCAGGCAGCTCGTAGCTCTTC-3′; SEQ ID NO: 385 NO: 386 RPLP0 5′-GGAATGTGGGCTTTGTGTTCACC-3′; SEQ 5 ′-AGGCCAGGACTCGTTTGTACC-3′; SEQ ID ID NO: 387 NO: 388 RPL8 5′-ACATCAAGGGCATCGTCAAGG-3′; SEQ ID 5′-TCTCTTTCTCCTGCACAGTCTTGG-3′; SEQ NO: 389 ID NO: 390 B2M 5′-TGCTCGCGCTACTCTCTCTTTC-3′; SEQ ID 5′-TCACATGGTTCACACGGCAG-3′; SEQ ID NO: 391 NO: 392 K10 5′-TGGCCTTCTCTCTGGAAATG-3′; SEQ ID 5′-TCATTTCCTCCTCGTGGTTC-3′; SEQ ID NO: 393 NO: 394 K14 5′-AGGTGACCATGCAGAACCTC-3′; SEQ ID 5′-CCTCGTGGTTCTTCTTCAGG-3′; SEQ ID NO: 395 NO: 396 MITF 5′-GAAATCTTGGGCTTGATGGA-3′; SEQ ID 5′-CCGAGGTTGTTGTTGAAGGT-3′; SEQ ID NO: 397 NO: 398 TYR 5′-CCATGGATAAAGCTGCCAAT-3′; SEQ ID 5′-GACACAGCAAGCTCACAAGC-3′; SEQ ID NO: 399 NO: 400 MLANA 5′-CACTCTTACACCACGGCTGA-3′; SEQ ID 5′-CATAAGCAGGTGGAGCATTG-3′; SEQ ID NO: 401 NO: 402 PMEL 5′-TTGTCCAGGGTATTGAAAGTGC-3′; SEQ 5′-GACAAGAGCAGAAGATGCGGG-3′; SEQ ID ID NO: 403 NO: 404 NES 5′-GCGTTGGAACAGAGGTTGGAG-3′; SEQ 5′-CAGGTGTCTCAAGGGTAGCAGG-3′; SEQ ID ID NO: 405 NO: 406 L1CAM 5′-CTTCCCTTTCGCCACAGTATG-3′; SEQ ID 5′-CCTCCTTCTCCTTCTTGCCACT-3′; SEQ ID NO: 407 NO: 408 GDF15 5′-AATGGCTCTCAGATGCTCCTGG-3′; SEQ 5′-GATTCTGCCAGCAGTTGGTCC-3′; SEQ ID ID NO: 409 NO: 410 ARPC1B 5′-ACCACAGCTTCCTGGTGGAG-3′; SEQ ID 5′-GAGCGGATGGGCTTCTTGATG-3′; SEQ ID NO: 411 NO: 412 FARP1 5′-AACGTGACCTTGTCTCCCAAC-3′; SEQ ID 5′-GCATGACATCGCCGATTCTT-3′; SEQ ID NO: 413 NO: 414 NTRK3 5′-TTCAACAAGCCCACCCACTAC-3′; SEQ ID 5′-GTTCTCAATGACAGGGATGCG-3′; SEQ ID NO: 415 NO: 416 CSK 5′-CATGGAATACCTGGAGGGCAAC-3′; SEQ 5′-CAGGTGCCAGCAGTTCTTCAT-3′; SEQ ID ID NO: 417 NO: 418 CD44 5′-TCTCAGAGCTTCTCTACATCAC-3′; SEQ ID 5′-CTGACGACTCCTTGTTCACCA-3′; SEQ ID NO: 419 NO: 420 SNX17 5′-TCACCTCCTCTGTACCATTGC-3′; SEQ ID 5′-CTCATCTCCAATGCCCTCGA-3′; SEQ ID NO: 421 NO: 422 PLAT 5′-TGCAATGAAGAGAGGGCTCTG-3′; SEQ ID 5′-CGTGGCCCTGGTATCTATTTCA-3′; SEQ ID NO: 423 NO: 424

The PCR reactions were performed using a high-fidelity polymerase (product name: ‘Phusion’, obtained from New England Biolabs). PCR amplification products were checked for correct size and subsequently gel purified using the Qiagen Gel Extraction kit. Purified PCR fragments were subcloned into the bacterial expression vector pJET1.2 using a commercially available kit (Fermentas). The subcloned fragments were subsequently checked by restriction digest and DNA sequencing. Bacterial clones harboring the pJET1.2 expression vector with the correct PCR insert (containing the desired amplicon for real time PCR primer pairs) were frozen and stored at −80° C. This was done to regenerate the same real time PCR standards over time.

Bacteria harboring the pJET1.2 expression vector with PCR inserts were cultured to generate sufficient amounts of vector. A small aliquot of the total retrieved expression vector with insert was linearized using the PvuI-HF restriction enzyme (from New England Biolabs). The digest was then purified using the Qiagen PCR purification kit. Linearized cDNA was diluted to a concentration of 20 ng/μL. One μL of each of a total of 71 linearized cDNA fragments (each at a 20 ng/μL concentration) were mixed and brought to a final volume of 1 mL to obtain standard S7.

Standard S7 was then diluted six times at a 1:10 ratio to obtained standards 51 to S6. Dilution was performed using ultrapure water obtained from Promega (Cat. No. P1193).

The following was performed to generate cDNA from FFPE samples. FFPE blocks were cut at 20 μm sections using a standard Leica microtome. For large pieces of tissue, 2×20 μm full sections were used for RNA retrieval. For smaller tissues, up to 5×20 μm sections were combined for RNA retrieval. RNA extraction was performed using the Qiagen RNA FFPE retrieval kit and a Qiagen QiaCube extraction robot. 0.5 to 1 μg of RNA with a 260/280 ratio of greater than 1.8 were transcribed into cDNA using the BioRad iScript cDNA Synthesis kit. All biospecimens were annotated with clinical data from Mayo Clinic databases. H&E stained sections were obtained for each block analyzed and digitalized using a high-resolution slide scanner.

Fluidigm RT-PCR was performed using a 96×96 format for high throughput analysis (i.e., 96 cDNAs were analyzed for 96 markers; 9216 data points). The primer pairs and cDNAs were prepared in a 96 well format. Standard curves were calculated for each primer pair. Copy numbers per 100,000 housekeeping genes were calculated for each primer pair and averaged per gene. This was initially done for cDNAs derived from FFPE-embedded skin. To correct for epidermal cell-derived cross-contamination, background signal per one copy of K14 (a basal keratinocyte marker) was calculated from FFPE-embedded normal skin samples for each primer pair and averaged. Experimental samples were then normalized first to 100,000 housekeeping genes and then background-corrected for epidermal cross-contamination based on K14 copy number. In particular, the keratinocyte correction factor used for each gene is set forth in Table E under the column titled “AVG per copy K14.”

The study design (Example 1) involved a comparison of the expression profile of ‘true’ benign pigmented skin lesions (nevi, n=73) with ‘true’ malignant melanomas of the skin. The latter comprised i) primary skin melanomas that were documented to metastasize, either to regional lymph nodes, to other areas of skin (in-transit), or to other organs; and ii) in-transit or comparison of nevi to in-transit melanoma metastases (n=54).

Tables C and D summarize the comparisons of the gene expressions between the 73 benign and 54 metastatic. Table A compares the ranked values using the Wilcoxon rank sum test, and Table E compares the dichotomized values (zero vs. >0) using the chi-square test.

A recursive partitioning approach was used to identify cut-points for the genes that would discriminate between these two groups. After partitioning the data at a cut-point of 45 for FN1, no further additional splits in the data based on the other genes were identified by this method.

Using a cutoff of 45 for FN1, the sensitivity was 92.6%, and the specificity was 98.6%. These results are provided in Tables 4 and 5 along with the next possible cutoff for FN1 at 124

TABLE 4 Frequency Percent Row Pct Col Pct Malignant Benign Total FN1 < 45 4 72 76 3.15 56.69 59.84 5.26 94.74 7.41 98.63 FN1 >= 45 50 1 51 39.37 0.79 40.16 98.04 1.96 92.59 1.37 Total 54 73 127 42.52 57.48 100.00

TABLE 5 Frequency Percent Row Pct Col Pct Malignant Benign Total FN1 < 124 8 73 81 6.30 57.48 63.78 9.88 90.12 14.81 100.00 FN1 >= 124 46 0 46 36.22 0.00 36.22 100.00 0.00 85.19 0.00 Total 54 73 127 42.52 57.48 100.00

The ability to further discriminate between the groups was assessed by considering SPP1 or ITGB3 in addition to FN1.

Benign vs. Malignant—Option 1 using FN1 and SPP1 (FIG. 5)

The results are set forth in Table 6.

TABLE 6 RULE for FIG. 5 Malignant Benign FN1 < 45 and SPP1 = 0 2 72 FN1 >= 45 52 1 or (FN1 < 45 and SPP1 > 0) Total 54 73 Benign Vs. Malignant—Option 2 Using FN1 and ITGB3 (FIG. 6)

The results are set forth in Table 7.

TABLE 7 RULE for FIG. 6 Malignant Benign FN1 < 45 and ITGB3 = 0 3 72 FN1 >= 45 51 1 or (FN1 < 45 and ITGB3 > 0) Total 54 73

If all three genes are included, the rule was as follows:

FN1<45 and SPP1=0 and ITGB3=0 denotes a negative test

-   -   vs.

all other combinations denotes a positive test.

This rule resulted in a specificity of 72/73 (98.6%), and a sensitivity of 53/54 (98.2%) (Table 8). Compared to a rule using FN1 alone, the specificity stayed the same but the sensitivity increased from 92.6% to 98.2% using this new rule.

TABLE 8 FN1 SPP1 ITGB3 malignant Frequency <45 Zero Zero No 72 <45 Zero Zero Yes 1 False Neg ID MM150 (case added from the Breslow file) >=45 Zero Zero No 1 False Pos ID N29 >=45 Zero Zero Yes 9 >=45 Zero >0 Yes 1 >=45 >0 Zero Yes 18 >=45 >0 >0 Yes 22 <45 Zero >0 Yes 1 <45 >0 Zero Yes 2

The rule was evaluated using 25 additional malignant patients who did not have mets (from the “Breslow” file). For 19 of these 25 patients, the rule was ‘negative’ (Table 9).

TABLE 9 FN1 SPP1 ITGB3 Frequency <45 Zero Zero 19 <45 >0 Zero 1 >=45 Zero Zero 2 >=45 >0 Zero 3 <45 1

The rule also was evaluated using 33 thin melanomas (Table 10). For 25 of these 33 patients, the rule was ‘negative’.

TABLE 10 FN1 SPP1 ITGB3 Frequency <45 Zero Zero 25 <45 Zero >0 1 >=45 Zero Zero 5 >=45 >0 Zero 2

TABLE C Comparison of gene expression between benign and malignant Benign Malignant (N = 73) (N = 54) p value CXCL1_AVG_NORM <0.0001 N 73 54 Mean (SD)  4.8 (18.4) 20.0 (26.1) Median   0.0   10.3 Q1, Q3 0.0, 0.0 0.3, 31.1 Range (0.0-141.7) (0.0-120.4) CSF2_AVG_NORM 0.0482 N 73 54 Mean (SD) 10.5 (44.1) 4.3 (8.4) Median   2.5   1.0 Q1, Q3 0.6, 7.0 0.0, 4.0  Range (0.0-375.0) (0.0-41.0) CCL2_AVG_NORM <0.0001 N 73 54 Mean (SD) 37.0 (99.4) 244.2 (360.9) Median   0.0  112.8 Q1, Q3 0.0, 9.1  7.2, 342.2 Range (0.0-572.0) (0.0-1777.1) IL8_AVG_NORM <0.0001 N 73 54 Mean (SD) 125.5 (671.3)  53.2 (160.8) Median   0.0   13.0 Q1, Q3 0.0, 0.0 2.1, 52.5 Range (0.0-5058.7) (0.0-1171.7) IL6_AVG_NORM <0.0001 N 73 54 Mean (SD)  9.9 (69.1) 21.6 (35.0) Median   0.0   8.8 Q1, Q3 0.0, 0.0 0.3, 25.2 Range (0.0-589.1) (0.0-152.3) ITGA5_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0)  9.8 (26.8) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 7.0  Range (0.0-0.0) (0.0-168.0) ITGA3_AVG_NORM <0.0001 N 73 54 Mean (SD)  3.2 (27.5) 168.2 (313.4) Median   0.0   50.2 Q1, Q3 0.0, 0.0  2.0, 160.5 Range (0.0-235.4) (0.0-1506.0) ITGA2_AVG_NORM 0.0007 N 73 54 Mean (SD) 0.0 (0.0)  2.6 (10.0) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 0.0  Range (0.0-0.0) (0.0-69.7) ITGAV_AVG_NORM <0.0001 N 73 54 Mean (SD)  3.3 (23.9) 22.0 (32.9) Median   0.0   8.0 Q1, Q3 0.0, 0.0 0.0, 31.0 Range (0.0-199.9) (0.0-176.8) ITGB3_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0) 43.6 (90.3) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 52.5 Range (0.0-0.0) (0.0-495.3) ITGB1_AVG_NORM <0.0001 N 73 54 Mean (SD) 29.9 (95.1) 616.2 (742.2) Median   0.0  400.2 Q1, Q3 0.0, 0.0 84.7, 869.0 Range (0.0-487.9) (0.0-3877.9) FN1_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.9 (15.6) 1570.9 (1949.8) Median   0.0  898.4 Q1, Q3 0.0, 0.0 299.5, 2186.1 Range (0.0-123.2) (0.0-11073.5) THBS1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0)  85.1 (136.1) Median   0.0   16.8 Q1, Q3 0.0, 0.0  0.0, 153.8 Range (0.0-0.0) (0.0-786.2) THBS2_AVG_NORM <0.0001 N 73 54 Mean (SD)  25.9 (113.4) 280.0 (513.5) Median   0.0   44.1 Q1, Q3 0.0, 0.0  0.0, 340.1 Range (0.0-729.2) (0.0-3030.5) THBS4_AVG_NORM <0.0001 N 73 54 Mean (SD)  38.5 (151.2) 228.2 (663.7) Median   0.0   22.5 Q1, Q3 0.0, 0.0 0.0, 97.9 Range (0.0-1130.3) (0.0-3977.7) VCAN_AVG_NORM <0.0001 N 73 54 Mean (SD)  3.0 (21.7) 202.4 (262.8) Median   0.0  103.4 Q1, Q3 0.0, 0.0  0.0, 283.5 Range (0.0-181.3) (0.0-1113.2) BGAN_AVG_NORM <0.0001 N 73 54 Mean (SD)  69.3 (121.0) 422.4 (573.1) Median   0.0  248.5 Q1, Q3  0.0, 97.9 113.5, 462.9  Range (0.0-496.3) (0.0-3348.1) SPP1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0) 1490.2 (3397.4) Median   0.0  338.1 Q1, Q3 0.0, 0.0  4.9, 1577.7 Range (0.0-0.0) (0.0-22427.0) TNC_AVG_NORM <0.0001 N 73 54 Mean (SD)  66.4 (240.1) 800.1 (808.7) Median   0.0  495.8 Q1, Q3 0.0, 0.0 174.5, 1322.9 Range (0.0-1393.3) (0.0-3162.2) SPARC_AVG_NORM <0.0001 N 73 54 Mean (SD)  843.7 (2222.8) 3208.4 (3182.6) Median   0.0 2895.8  Q1, Q3 0.0, 0.0 407.2, 5216.3 Range (0.0-11175.6) (0.0-13631.9) AGRN_AVG_NORM <0.0001 N 73 54 Mean (SD)  4.7 (18.1) 51.2 (53.8) Median   0.0   42.1 Q1, Q3 0.0, 0.0 10.7, 69.7  Range (0.0-121.7) (0.0-242.0) CTGF_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.4 (3.6)  90.9 (231.6) Median   0.0   22.1 Q1, Q3 0.0, 0.0  0.0, 125.9 Range (0.0-30.6) (0.0-1631.4) CYR61_AVG_NORM <0.0001 N 73 54 Mean (SD)  4.8 (13.0) 27.2 (39.2) Median   0.0   18.7 Q1, Q3 0.0, 0.0 4.9, 32.2 Range (0.0-70.4) (0.0-267.2) LAMA3_AVG_NORM 0.0004 N 73 54 Mean (SD) 1.1 (9.0) 1.2 (2.9) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 0.0  Range (0.0-76.8) (0.0-11.3) LAMC1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0)  70.6 (159.4) Median   0.0   28.4 Q1, Q3 0.0, 0.0 0.0, 99.3 Range (0.0-0.0) (0.0-1136.2) LAMB1_AVG_NORM <0.0001 N 73 54 Mean (SD)  9.2 (38.4) 221.1 (354.3) Median   0.0   73.1 Q1, Q3 0.0, 0.0  0.0, 339.8 Range (0.0-248.8) (0.0-1877.6) LAMA1_AVG_NORM <0.0001 N 73 54 Mean (SD)  5.7 (14.5)  65.4 (149.0) Median   0.0   10.6 Q1, Q3 0.0, 0.0 0.0, 49.0 Range (0.0-76.5) (0.0-754.3) LAMC2_AVG_NORM 0.0003 N 73 54 Mean (SD) 0.0 (0.0)  4.0 (15.3) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 0.0  Range (0.0-0.0) (0.0-91.1) LAMB3_AVG_NORM 0.1473 N 73 54 Mean (SD) 33.5 (60.3) 32.2 (54.5) Median   0.0   12.1 Q1, Q3  0.0, 44.6 0.0, 37.0 Range (0.0-323.9) (0.0-246.0) COL1A1_AVG_NORM <0.0001 N 73 54 Mean (SD) 1534.4 (4365.3) 4191.6 (5865.9) Median   0.0 1704.4  Q1, Q3 0.0, 0.0  0.0, 6850.9 Range (0.0-22510.2) (0.0-31867.0) COL4A1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.0 (0.0) 211.8 (344.1) Median   0.0  118.4 Q1, Q3 0.0, 0.0  2.3, 261.2 Range (0.0-0.0) (0.0-1774.4) COL18A1_AVG_NORM <0.0001 N 73 54 Mean (SD)  94.2 (783.4) 22.8 (38.8) Median   0.0   4.1 Q1, Q3 0.0, 0.0 0.0, 34.4 Range (0.0-6695.7) (0.0-208.8) LOX_AVG_NORM 0.0003 N 73 54 Mean (SD)  37.7 (132.8)  65.0 (113.9) Median   0.0   3.5 Q1, Q3 0.0, 0.0 0.0, 58.0 Range (0.0-991.2) (0.0-443.3) LOXL1_AVG_NORM <0.0001 N 73 54 Mean (SD) 0.8 (7.1) 39.6 (60.3) Median   0.0   18.5 Q1, Q3 0.0, 0.0 0.0, 65.0 Range (0.0-60.4) (0.0-349.0) LOXL2_AVG_NORM <0.0001 N 73 54 Mean (SD)  43.3 (356.8)  68.5 (129.9) Median   0.0   22.1 Q1, Q3 0.0, 0.0 0.0, 89.1 Range (0.0-3048.4) (0.0-821.4) LOXL3_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.2 (12.3) 28.4 (71.1) Median   0.0   9.2 Q1, Q3 0.0, 0.0 2.5, 29.4 Range (0.0-89.7) (0.0-507.5) LOXL4_AVG_NORM 0.0010 N 73 54 Mean (SD) 33.8 (91.0) 129.1 (300.4) Median   0.0   9.1 Q1, Q3  0.0, 10.2 0.0, 67.0 Range (0.0-529.2) (0.0-1230.0) PLOD1_AVG_NORM <0.0001 N 73 54 Mean (SD)  33.7 (116.5) 420.3 (532.2) Median   0.0  242.3 Q1, Q3 0.0, 0.0 90.2, 659.3 Range (0.0-878.2) (0.0-3336.8) PLOD2_AVG_NORM <0.0001 N 73 54 Mean (SD) 44.5 (151.7)  314.8 (1284.4) Median   0.0   53.7 Q1, Q3 0.0, 0.0  2.3, 103.3 Range (0.0-1124.0) (0.0-9110.5) PLOD3_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.7 (11.9) 68.0 (81.2) Median   0.0   38.3 Q1, Q3 0.0, 0.0  4.2, 101.9 Range (0.0-87.4) (0.0-330.2) PCOLCE2_AVG_NORM 0.0010 N 73 54 Mean (SD)  7.7 (25.8)  6.4 (14.9) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 3.1  Range (0.0-104.8) (0.0-68.4) PCOLCE_AVG_NORM 0.0232 N 73 54 Mean (SD)  92.1 (159.7) 170.4 (339.4) Median   0.0   40.9 Q1, Q3  0.0, 122.2  0.0, 175.1 Range (0.0-699.2) (0.0-1945.2) PTK2_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.8 (14.4) 76.6 (81.8) Median   0.0   70.0 Q1, Q3 0.0, 0.0  0.0, 127.7 Range (0.0-116.5) (0.0-323.3) CSRC_AVG_NORM 0.0001 N 73 54 Mean (SD) 19.0 (40.9) 45.1 (65.9) Median   0.3   19.6 Q1, Q3  0.0, 24.8 4.2, 46.6 Range (0.0-266.6) (0.0-290.2) CDKN1A_AVG_NORM 0.0005 N 73 54 Mean (SD)  78.5 (150.9) 181.0 (271.7) Median   0.0   84.2 Q1, Q3  0.0, 118.9  0.0, 253.3 Range (0.0-788.2) (0.0-1083.2) CDKN2A_AVG_NORM 0.0002 N 73 54 Mean (SD)  6.1 (19.6)  9.7 (25.8) Median   0.0   1.0 Q1, Q3 0.0, 0.0 0.0, 6.9  Range (0.0-113.2) (0.0-175.1) TP53_AVG_NORM <0.0001 N 73 54 Mean (SD) 40.6 (98.6) 231.2 (289.8) Median   0.0  166.9 Q1, Q3 0.0, 0.0  0.0, 359.9 Range (0.0-410.8) (0.0-1722.4) YAP_AVG_NORM <0.0001 N 73 54 Mean (SD)  7.8 (36.6) 112.4 (161.4) Median   0.0   63.1 Q1, Q3 0.0, 0.0  0.0, 173.5 Range (0.0-246.3) (0.0-769.0) TAZ_AVG_NORM <0.0001 N 73 54 Mean (SD) 12.2 (27.9) 32.8 (44.3) Median   0.0   15.0 Q1, Q3 0.0, 0.7 0.0, 49.0 Range (0.0-122.7) (0.0-186.4) MITF_AVG_NORM <0.0001 N 73 54 Mean (SD) 251.0 (399.5) 569.8 (494.8) Median   45.5  467.3 Q1, Q3  0.0, 331.5 184.9, 777.8  Range (0.0-2143.3) (0.0-2200.0) MLANA_AVG_NORM 0.1823 N 73 54 Mean (SD) 3596.0 (3671.3) 4865.4 (4966.1) Median 2446.8  2803.5  Q1, Q3  950.9, 5019.4 1210.7, 6773.0  Range (14.0-17180.3) (62.8-19672.1) TYR_AVG_NORM 0.0040 N 73 54 Mean (SD) 349.7 (301.8) 839.8 (996.3) Median  254.3  515.1 Q1, Q3 119.5, 527.5 161.0, 1244.9 Range (0.0-1169.8) (2.0-5500.0) POSTN_AVG_NORM 0.0001 N 73 54 Mean (SD) 1138.7 (2155.7) 1933.9 (2318.1) Median  191.6 1252.0  Q1, Q3   0.0, 1449.9 397.4, 2457.4 Range (0.0-11078.1) (0.0-11193.2) FBLN2_AVG_NORM <0.0001 N 73 54 Mean (SD)  2.1 (17.3) 26.5 (42.2) Median   0.0   0.0 Q1, Q3 0.0, 0.0 0.0, 48.8 Range (0.0-148.2) (0.0-150.9)

TABLE D Comparison of gene expression between benign and malignant Benign Malignant (N = 73) (N = 54) p value CXCL1_AVG_NORM01 <0.0001 Zero 58 (79.5%) 12 (22.2%) >0 15 (20.5%) 42 (77.8%) CSF2_AVG_NORM01 0.0398 Zero 15 (20.5%) 20 (37.0%) >0 58 (79.5%) 34 (63.0%) CCL2_AVG_NORM01 <0.0001 Zero 53 (72.6%) 12 (22.2%) >0 20 (27.4%) 42 (77.8%) IL8_AVG_NORM01 <0.0001 Zero 63 (86.3%) 10 (18.5%) >0 10 (13.7%) 44 (81.5%) IL6_AVG_NORM01 <0.0001 Zero 65 (89.0%) 13 (24.1%) >0  8 (11.0%) 41 (75.9%) ITGA5_AVG_NORM01 <0.0001 Zero  73 (100.0%) 38 (70.4%) >0 0 (0.0%) 16 (29.6%) ITGA3_AVG_NORM01 <0.0001 Zero 72 (98.6%) 13 (24.1%) >0 1 (1.4%) 41 (75.9%) ITGA2_AVG_NORM01 0.0007 Zero  73 (100.0%) 46 (85.2%) >0 0 (0.0%)  8 (14.8%) ITGAV_AVG_NORM01 <0.0001 Zero 71 (97.3%) 24 (44.4%) >0 2 (2.7%) 30 (55.6%) ITGB3_AVG_NORM01 <0.0001 Zero  73 (100.0%) 30 (55.6%) >0 0 (0.0%) 24 (44.4%) ITGB1_AVG_NORM01 <0.0001 Zero 64 (87.7%) 11 (20.4%) >0  9 (12.3%) 43 (79.6%) FN1_AVG_NORM01 <0.0001 Zero 69 (94.5%) 2 (3.7%) >0 4 (5.5%) 52 (96.3%) THBS1_AVG_NORM01 <0.0001 Zero  73 (100.0%) 24 (44.4%) >0 0 (0.0%) 30 (55.6%) THBS2_AVG_NORM01 <0.0001 Zero 67 (91.8%) 23 (42.6%) >0 6 (8.2%) 31 (57.4%) THBS4_AVG_NORM01 <0.0001 Zero 58 (79.5%) 15 (27.8%) >0 15 (20.5%) 39 (72.2%) VCAN_AVG_NORM01 <0.0001 Zero 71 (97.3%) 16 (29.6%) >0 2 (2.7%) 38 (70.4%) BGAN_AVG_NORM01 <0.0001 Zero 42 (57.5%)  7 (13.0%) >0 31 (42.5%) 47 (87.0%) SPP1_AVG_NORM01 <0.0001 Zero  73 (100.0%) 12 (22.2%) >0 0 (0.0%) 42 (77.8%) TNC_AVG_NORM01 <0.0001 Zero 60 (82.2%) 3 (5.6%) >0 13 (17.8%) 51 (94.4%) SPARC_AVG_NORM01 <0.0001 Zero 57 (78.1%) 13 (24.1%) >0 16 (21.9%) 41 (75.9%) AGRN_AVG_NORM01 <0.0001 Zero 59 (80.8%) 5 (9.3%) >0 14 (19.2%) 49 (90.7%) CTGF_AVG_NORM01 <0.0001 Zero 72 (98.6%) 21 (38.9%) >0 1 (1.4%) 33 (61.1%) CYR61_AVG_NORM01 <0.0001 Zero 56 (76.7%)  9 (16.7%) >0 17 (23.3%) 45 (83.3%) LAMA3_AVG_NORM01 0.0003 Zero 72 (98.6%) 43 (79.6%) >0 1 (1.4%) 11 (20.4%) LAMC1_AVG_NORM01 <0.0001 Zero  73 (100.0%) 24 (44.4%) >0 0 (0.0%) 30 (55.6%) LAMB1_AVG_NORM01 <0.0001 Zero 66 (90.4%) 22 (40.7%) >0 7 (9.6%) 32 (59.3%) LAMA1_AVG_NORM01 <0.0001 Zero 57 (78.1%) 16 (29.6%) >0 16 (21.9%) 38 (70.4%) LAMC2_AVG_NORM01 0.0003 Zero  73 (100.0%) 45 (83.3%) >0 0 (0.0%)  9 (16.7%) LAMB3_AVG_NORM01 0.0061 Zero 45 (61.6%) 20 (37.0%) >0 28 (38.4%) 34 (63.0%) COL1A1_AVG_NORM01 <0.0001 Zero 60 (82.2%) 17 (31.5%) >0 13 (17.8%) 37 (68.5%) COL4A1_AVG_NORM01 <0.0001 Zero  73 (100.0%) 13 (24.1%) >0 0 (0.0%) 41 (75.9%) COL18A1_AVG_NORM01 <0.0001 Zero 64 (87.7%) 18 (33.3%) >0  9 (12.3%) 36 (66.7%) LOX_AVG_NORM01 <0.0001 Zero 60 (82.2%) 26 (48.1%) >0 13 (17.8%) 28 (51.9%) LOXL1_AVG_NORM01 <0.0001 Zero 72 (98.6%) 23 (42.6%) >0 1 (1.4%) 31 (57.4%) LOXL2_AVG_NORM01 <0.0001 Zero 70 (95.9%) 19 (35.2%) >0 3 (4.1%) 35 (64.8%) LOXL3_AVG_NORM01 <0.0001 Zero 69 (94.5%) 10 (18.5%) >0 4 (5.5%) 44 (81.5%) LOXL4_AVG_NORM01 0.0006 Zero 53 (72.6%) 23 (42.6%) >0 20 (27.4%) 31 (57.4%) PLOD1_AVG_NORM01 <0.0001 Zero 59 (80.8%) 12 (22.2%) >0 14 (19.2%) 42 (77.8%) PLOD2_AVG_NORM01 <0.0001 Zero 59 (80.8%) 10 (18.5%) >0 14 (19.2%) 44 (81.5%) PLOD3_AVG_NORM01 <0.0001 Zero 66 (90.4%) 11 (20.4%) >0 7 (9.6%) 43 (79.6%) PCOLCE2_AVG_NORM01 0.0002 Zero 66 (90.4%) 34 (63.0%) >0 7 (9.6%) 20 (37.0%) PCOLCE_AVG_NORM01 0.0036 Zero 42 (57.5%) 17 (31.5%) >0 31 (42.5%) 37 (68.5%) PTK2_AVG_NORM01 <0.0001 Zero 67 (91.8%) 16 (29.6%) >0 6 (8.2%) 38 (70.4%) CSRC_AVG_NORM01 0.0001 Zero 36 (49.3%)  9 (16.7%) >0 37 (50.7%) 45 (83.3%) CDKN1A_AVG_NORM01 0.0001 Zero 48 (65.8%) 16 (29.6%) >0 25 (34.2%) 38 (70.4%) CDKN2A_AVG_NORM01 <0.0001 Zero 57 (78.1%) 23 (42.6%) >0 16 (21.9%) 31 (57.4%) TP53_AVG_NORM01 <0.0001 Zero 59 (80.8%) 16 (29.6%) >0 14 (19.2%) 38 (70.4%) YAP_AVG_NORM01 <0.0001 Zero 68 (93.2%) 22 (40.7%) >0 5 (6.8%) 32 (59.3%) TAZ_AVG_NORM01 <0.0001 Zero 54 (74.0%) 19 (35.2%) >0 19 (26.0%) 35 (64.8%) MITF_AVG_NORM01 <0.0001 Zero 26 (35.6%) 2 (3.7%) >0 47 (64.4%) 52 (96.3%) MLANA_AVG_NORM01 >0  73 (100.0%)  54 (100.0%) TYR_AVG_NORM01 0.2202 Zero 2 (2.7%) 0 (0.0%) >0 71 (97.3%)  54 (100.0%) POSTN_AVG_NORM01 <0.0001 Zero 32 (43.8%) 4 (7.4%) >0 41 (56.2%) 50 (92.6%) FBLN2_AVG_NORM01 <0.0001 Zero 71 (97.3%) 31 (57.4%) >0 2 (2.7%) 23 (42.6%)

TABLE E MM79_CN MM80_CN MM81_CN MM82_CN AVG AVG AVG AVG AVG per per copy per copy per copy per copy copy K14 K14 K14 K14 K14 STDEV % STDEV KRT14_AVG_NORM 1 1 1 1 1 0.000 KRT10_AVG_NORM 2.209 2.229 2.92 3.015 2.593 0.434 17% MITF_AVG_NORM 0.021 0.018 0.016 0.015 0.018 0.003 15% MLANA_AVG_NORM 0.021 0.018 0.016 0.015 0.018 0.003 15% TYR_AVG_NORM 0.004 0.002 0.002 0.001 0.002 0.001 56% PMEL_AVG_NORM 0.025 0.027 0.03 0.018 0.025 0.005 20% FN1_AVG_NORM 0.077 0.065 0.035 0.042 0.055 0.020 36% SPARC_AVG_NORM 1.294 1.143 0.568 1.707 1.178 0.471 40% AGRN_AVG_NORM 0.004 0.006 0.003 0.002 0.004 0.002 46% THBS1_AVG_NORM 0.064 0.015 0.018 0.005 0.026 0.026 103%  THBS2_AVG_NORM 0.366 0.061 0.104 0.057 0.147 0.148 100%  THBS4_AVG_NORM 0.018 0.006 0.005 0.001 0.008 0.007 98% VCAN_AVG_NORM 0.095 0.034 0.04 0.027 0.049 0.031 64% BGAN_AVG_NORM 0.015 0.027 0.014 0.015 0.018 0.006 35% COL1A1_AVG_NORM 1.695 3.44 0.689 6.695 3.130 2.635 84% COL4A1_AVG_NORM 0.069 0.026 0.03 0.016 0.035 0.023 66% COL4A2_AVG_NORM 0.115 0.042 0.041 0.004 0.051 0.046 92% COL18A1_AVG_NORM 0.015 0.009 0.005 0.002 0.008 0.006 73% CTGF_AVG_NORM 0.012 0.008 0.016 0.004 0.010 0.005 52% LOX_AVG_NORM 0.029 0.021 0.028 0.021 0.025 0.004 18% LOXL1_AVG_NORM 0.015 0.009 0.016 0.015 0.014 0.003 23% LOXL2_AVG_NORM 0.016 0.011 0.008 0.006 0.010 0.004 42% LOXL3_AVG_NORM 0.003 0.002 0.002 0.001 0.002 0.001 41% LOXL4_AVG_NORM 0.02 0.004 0.003 0.001 0.007 0.009 125%  PLOD2_AVG_NORM 0.018 0.014 0.007 0.001 0.010 0.008 75% PLOD1_AVG_NORM 0.069 0.053 0.026 0.017 0.041 0.024 58% SPP1_AVG_NORM 0.092 0.002 0.007 0 0.025 0.045 177%  TNC_AVG_NORM 0.025 0.02 0.027 0.013 0.021 0.006 29% PCOLCE2_AVG_NORM 0.011 0.001 0.006 0 0.005 0.005 113%  PCOLCE_AVG_NORM 0.028 0.049 0.032 0.04 0.037 0.009 25% PLOD3_AVG_NORM 0.03 0.006 0.007 0.002 0.011 0.013 113%  ITGB3_AVG_NORM 0.03 0.006 0.007 0.002 0.011 0.013 113%  ITGB1_AVG_NORM 0.164 0.054 0.074 0.038 0.083 0.056 68% FBLN2_AVG_NORM 0.049 0.022 0.02 0.016 0.027 0.015 56% CYR61_AVG_NORM 0.006 0.002 0.003 0 0.003 0.003 91% ITGA5_AVG_NORM 0.011 0.005 0.007 0.003 0.007 0.003 53% ITGA3_AVG_NORM 0.016 0.008 0.006 0.008 0.010 0.004 47% ITGA2_AVG_NORM 0.08 0.034 0.019 0.084 0.054 0.033 60% ITGAV_AVG_NORM 0.013 0.005 0.003 0.003 0.006 0.005 79% CSRC_AVG_NORM 0.006 0.003 0.005 0.001 0.004 0.002 59% PTK2_AVG_NORM 0.035 0.02 0.011 0.009 0.019 0.012 63% POSTN_AVG_NORM 0.077 0.092 0.117 0.193 0.120 0.052 43% YAP_AVG_NORM 0.079 0.029 0.033 0.031 0.043 0.024 56% CXCL1_AVG_NORM 0.002 0 0 0 0.001 0.001 200%  CSF2_AVG_NORM 0.002 0 0 0 0.001 0.001 200%  CCL2_AVG_NORM 0.039 0.018 0.013 0.008 0.020 0.014 70% IL8_AVG_NORM 0.003 0 0.001 0 0.001 0.001 141%  IL6_AVG_NORM 0.001 0 0 0 0.000 0.001 200%  LAMA3_AVG_NORM 0.038 0.012 0.021 0.011 0.021 0.013 61% TP53_AVG_NORM 0.08 0.04 0.039 0.052 0.053 0.019 36% CDKN1A_AVG_NORM 0.057 0.029 0.037 0.014 0.034 0.018 52% CDKN2A_AVG_NORM 0.003 0.001 0.001 0 0.001 0.001 101%  TAZ_AVG_NORM 0.026 0.008 0.008 0.003 0.011 0.010 90% LAMC1_AVG_NORM 0.062 0.013 0.016 0.008 0.025 0.025 101%  LAMB1_AVG_NORM 0.046 0.019 0.026 0.008 0.025 0.016 65% LAMA1_AVG_NORM 0.007 0 0.001 0 0.002 0.003 168%  LAMC2_AVG_NORM 0.034 0.009 0.012 0.016 0.018 0.011 63% LAMB3_AVG_NORM 0.042 0.016 0.026 0.017 0.025 0.012 48% PLAT_AVG_NORM 0.032 0.02 0.034 0.04 0.032 0.001 27% CSK_AVG_NORM 0.027 0.034 0.021 0.041 0.031 0.001 28% GDF15_AVG_NORM 0.029 0.019 0.033 0.019 0.025 0.001 28% FARP1_AVG_NORM 0.019 0.029 0.022 0.031 0.025 0.001 22% ARPC1B_AVG_NORM 0.015 0.03 0.042 0.018 0.026 0.012 47% NES_AVG_NORM 0.114 0.125 0.112 0.084 0.109 0.017 16% NTRK3_AVG_NORM 0.021 0.025 0.022 0.033 0.025 0.001 25% SNX17_AVG_NORM 0.112 0.099 0.089 0.123 0.106 0.015 14% L1CAM_AVG_NORM 0.017 0.04 0.01 0.024 0.023 0.013 56% CD44_AVG_NORM 0.112 0.089 0.09 0.123 0.104 0.017 16%

The results provided herein demonstrate the development of a method for determining absolute levels (copy numbers) of genes of interest (e.g., FN-associated genes) from paraffin-embedded tissue by generating a highly defined internal standard that can be regenerated indefinitely. This standardization approach can allow for the comparison of results from independent experiments and thus, allows for extensive validation. The RT-PCR not only produced strong signals from highly degraded RNA due to FFPE embedding, but also was amendable to high-throughput analysis and was highly cost effective. While the methods provided herein were validated for melanoma, these methods are likely applicable to other human cancers. The results provided herein also demonstrate the discrimination between benign and malignant pigmented lesions based on multiple markers.

Example 3—Additional Marker Panel

A test kit panel was designed to include primers for assessing expression levels of eight marker genes (ITGB3, TNC, SPP1, SPARC, PLAT, COL4A1, PLOD3, and PTK2) as well as three housekeeping genes (ACTB, RPLP0, and RPL8), one keratinocyte markers (K14) to assess keratinocyte contamination, and two melanocyte markers (MLANA and MITF) to assess melanocyte content in the skin sections. The primers designed for this collection are set forth in Table 11.

TABLE 11 Primers for marker panel kit. Primer SEQ pair Direc- ID Gene name tion Sequence NO:  ACTB ACTB-G -F TGCTATCCCTGTACGCCTCT 433 ACTB-G -R GAGTCCATCACGATGCCAGT 434 ACTB ACTB-H -F GGACTTCGAGCAAGAGATGG 435 ACTB-H -R CTTCTCCAGGGAGGAGCTG 436 ACTB ACTB-I -F GGCTACAGCTTCACCACCAC 425 ACTB-I -R TAATGTCACGCACGATTTCC 426 RPLP0 RPLP0-B -F AACTCTGCATTCTCGCTTCC   9 RPLP0-B -R GCAGACAGACACTGGCAACA  10 RPLP0 RPLP0-C -F GCACCATTGAAATCCTGAGTG  11 RPLP0-C -R GCTCCCACTTTGTCTCCAGT  12 RPL8 RPL8-B -F ACAGAGCTGTGGTTGGTGTG  19 RPL8-B -R TTGTCAATTCGGCCACCT  20 RPL8 RPL8-E -F ACTGCTGGCCACGAGTACG  17 RPL8-E -R ATGCTCCACAGGATTCATGG  18 KRT14 KRT14-D -F TCCGCACCAAGTATGAGACA  39 KRT14-D -R ACTCATGCGCAGGTTCAACT  40 KRT14 KRT14-F -F GATGCAGATTGAGAGCCTGA 437 KRT14-F -R TTCTTCAGGTAGGCCAGCTC 438 MLANA MLANA-C -F GAGAAAAACTGTGAACCTGTGG  53 MLANA-C -R ATAAGCAGGTGGAGCATTGG  54 MITF MITF-B -F CGGCATTTGTTGCTCAGAAT  47 MITF-B -R GAGCCTGCATTTCAAGTTCC  48 ITGB3 ITGB3-A -F AAGAGCCAGAGTGTCCCAAG 159 ITGB3-A -R ACTGAGAGCAGGACCACCA 160 ITGB3 ITGB3-B -F CTTCTCCTGTGTCCGCTACAA 161 ITGB3-B -R CATGGCCTGAGCACATCTC 162 PLAT PLAT-C -F CCCAGCCAGGAAATCCAT 427 PLAT-C -R CTGGCTCCTCTTCTGAATCG 428 PLAT PLAT-D -F CAGTGCCTGTCAAAAGTTGC 429 PLAT-D -R CCCCGTTGAAACACCTTG 430 PLAT PLAT-E -F GAAGGATTTGCTGGGAAGTG 441 PLAT-E -R CGTGGCCCTGGTATCTATTT 442 PLOD3 PLOD3-D -F GGAAGGAATCGTGGAGCAG 111 PLOD3-D -R CAGCAGTGGGAACCAGTACA 112 PTK2 PTK2-D -F GAGACCATTCCCCTCCTACC 119 PTK2-D -R GCTTCTGTGCCATCTCAATCT 120 CDKN2A CDKN2A1-C -F AGGAGCCAGCGTCTAGGG 219 CDKN2A1-C -R CTGCCCATCATCATGACCT 220 CDKN2A CDKN2A2-C -F AACGCACCGAATAGTTACGG 221 CDKN2A2-C -R CATCATCATGACCTGGATCG 222

One purpose of the kit was to differentiate between melanoma with high and low risk of regional metastasis, and to appropriately select patients for surgical procedures such as sentinel lymph node biopsy (SLNB) or total lymphadenectomy. Another purpose of this kit was to estimate disease-free survival, disease relapse, or likelihood of death from melanoma. To study the ability of these methods to discriminate between melanoma with high and low risk of metastasis and to establish superiority to established methods, a cohort of 158 patients between October 1998 and June 2013 were identified as having been diagnosed with high-risk melanoma and as having underwent SLNB with the intention to assess metastatic potential of the tumor. Of note, high-risk melanoma by current criteria are defined as melanoma with an invasion depth (Breslow depth) of ≧1 mm; or melanoma with an invasion depth of 0.75 to 0.99 mm plus the presence of either one of the following three risk factors: >0 mitotic figures/mm²; tumor ulceration present; patient age <40 years.

All 158 patients met the criteria for high risk. 136 patients had a Breslow Depth ≧1 mm. 22 patients had a Breslow Depth between 0.75 and 0.99 and had at least 1 of the aforementioned 3 risk factors (ulceration, mitotic rate >0, age <40). Of the 158 patients, 36 (22.8%) had a melanoma-positive SLNB.

To select genes for a test kit from a pool of genes, the expression level of 52 genes (variables) was initially determined and dichotomized as zero vs. >zero and evaluated for an association with positive SLNB using the chi-square test for a 2×2 contingency level. The genes are ordered based on the value of the chi-square test statistic (Table 12).

TABLE 12 Value of the chi-square test statistic variable ITGB3 68.3522 SPP1 25.8460 LOXL3 16.7683 PLAT 16.5721 LAMB1 15.7544 YAP 13.4049 PLOD3 12.6062 TP53 12.3662 COL4A1 11.8336 TNC 11.3862 IL8 10.4697 ITGA5 10.3561 COL1A1 10.0006 VCAN 9.3250 PLOD1 8.6959 FN1 8.4857 PTK2 7.9874 ITGAV 7.7181 LOXL1 7.2109 LOXL2 6.6348 ITGB1 6.3556 CDKN1A 6.3117 CTGF 6.2588 GDF15 5.96939 CSRC 5.4435 ITGA2 5.0326 ITGA3 4.0603 LOX 3.8697 COL18A1 3.3392 IL6 3.0435 DSPP 2.7822 NTRK3 2.7822 LOXL4 2.7279 THBS2 2.5110 SPARC 1.9884 PCOLCE2 1.6499 AGRN 1.6118 CXCL1 1.3483 TAZ 1.3458 THBS4 1.1281 PCOLCE 0.9198 FBLN2 0.9198 LAMC2 0.9157 CCL2 0.8701 CDKN2A 0.6047 CSF2 0.5408 CYR61 0.4713 BGAN 0.4364 LAMA3 0.3455 POSTN 0.1902 LAMB3 0.1058 PLOD2 0.0152

As can be deduced from the chi-square test statistic, ITGB3 was highly discriminatory between melanoma with and without regional lymph node metastasis. The n (%) with a positive SLNB for those with no expression vs. expression level >0 was summarized (Table 13).

TABLE 13 Overall Positive No. (% of 158) No. (% of each row) FN1_01 Zero 110 (69.6%)  18 (16.4%) >0 48 (30.4%) 18 (37.5%) SPP1_01 Zero 93 (58.9%) 8 (8.6%) >0 65 (41.1%) 28 (43.1%) ITGB3_01 Zero 107 (67.7%)  4 (3.7%) >0 51 (32.3%) 32 (62.7%) TNC_01 Zero 114 (72.2%)  18 (15.8%) >0 44 (27.8%) 18 (40.9%) PLAT_01 Missing 18  0 Zero 83 (59.3%) 11 (13.3%) >0 57 (40.7%) 25 (43.9%) COL4A1_01 Zero 111 (70.3%)  17 (15.3%) >0 47 (29.7%) 19 (40.4%) SPARC_01 Missing 4 0 Zero 138 (89.6%)  30 (21.7%) >0 16 (10.4%)  6 (37.5%) AGRN_01 Missing 4 0 Zero 23 (14.9%)  3 (13.0%) >0 131 (85.1%)  33 (25.2%) THBS1_01 Missing 135  33  Zero 18 (78.3%) 0 (0.0%) >0  5 (21.7%)  3 (60.0%) THBS2_01 Missing 4 0 Zero 114 (74.0%)  23 (20.2%) >0 40 (26.0%) 13 (32.5%) THBS4_01 Missing 4 0 Zero 136 (88.3%)  30 (22.1%) >0 18 (11.7%)  6 (33.3%) VCAN_01 Missing 4 0 Zero 137 (89.0%)  27 (19.7%) >0 17 (11.0%)  9 (52.9%) BGAN_01 Missing 4 0 Zero 97 (63.0%) 21 (21.6%) >0 57 (37.0%) 15 (26.3%) COL1A1_01 Missing 4 0 Zero 145 (94.2%)  30 (20.7%) >0 9 (5.8%)  6 (66.7%) COL18A1_01 Missing 4 0 Zero 146 (94.8%)  32 (21.9%) >0 8 (5.2%)  4 (50.0%) CTGF_01 Missing 4 0 Zero 128 (83.1%)  25 (19.5%) >0 26 (16.9%) 11 (42.3%) LOX_01 Missing 4 0 Zero 149 (96.8%)  33 (22.1%) >0 5 (3.2%)  3 (60.0%) LOXL1_01 Missing 4 0 Zero 146 (94.8%)  31 (21.2%) >0 8 (5.2%)  5 (62.5%) LOXL2_01 Missing 4 0 Zero 115 (74.7%)  21 (18.3%) >0 39 (25.3%) 15 (38.5%) LOXL3_01 Missing 4 0 Zero 67 (43.5%) 5 (7.5%) >0 87 (56.5%) 31 (35.6%) LOXL4_01 Missing 4 0 Zero 122 (79.2%)  25 (20.5%) >0 32 (20.8%) 11 (34.4%) PLOD2_01 Missing 4 0 Zero 136 (88.3%)  32 (23.5%) >0 18 (11.7%)  4 (22.2%) PLOD1_01 Missing 4 0 Zero 111 (72.1%)  19 (17.1%) >0 43 (27.9%) 17 (39.5%) PCOLCE2_01 Missing 4 0 Zero 144 (93.5%)  32 (22.2%) >0 10 (6.5%)   4 (40.0%) PCOLCE_01 Missing 4 0 Zero 139 (90.3%)  31 (22.3%) >0 15 (9.7%)   5 (33.3%) PLOD3_01 Missing 4 0 Zero 109 (70.8%)  17 (15.6%) >0 45 (29.2%) 19 (42.2%) ITGB1_01 Missing 4 0 Zero 62 (40.3%)  8 (12.9%) >0 92 (59.7%) 28 (30.4%) FBLN2_01 Missing 4 0 Zero 139 (90.3%)  31 (22.3%) >0 15 (9.7%)   5 (33.3%) CYR61_01 Missing 4 0 Zero 50 (32.5%) 10 (20.0%) >0 104 (67.5%)  26 (25.0%) ITGA5_01 Missing 4 0 Zero 135 (87.7%)  26 (19.3%) >0 19 (12.3%) 10 (52.6%) ITGA3_01 Missing 4 0 Zero 56 (36.4%)  8 (14.3%) >0 98 (63.6%) 28 (28.6%) ITGA2_01 Missing 4 0 Zero 139 (90.3%)  29 (20.9%) >0 15 (9.7%)   7 (46.7%) ITGAV_01 Missing 4 0 Zero 120 (77.9%)  22 (18.3%) >0 34 (22.1%) 14 (41.2%) CSRC_01 Missing 4 0 Zero 90 (58.4%) 15 (16.7%) >0 64 (41.6%) 21 (32.8%) PTK2_01 Missing 4 0 Zero 61 (39.6%)  7 (11.5%) >0 93 (60.4%) 29 (31.2%) POSTN_01 Missing 4 0 Zero 103 (66.9%)  23 (22.3%) >0 51 (33.1%) 13 (25.5%) YAP_01 Missing 4 0 Zero 137 (89.0%)  26 (19.0%) >0 17 (11.0%) 10 (58.8%) CXCL1_01 Missing 4 0 Zero 94 (61.0%) 19 (20.2%) >0 60 (39.0%) 17 (28.3%) CSF2_01 Missing 4 0 Zero 131 (85.1%)  32 (24.4%) >0 23 (14.9%)  4 (17.4%) CCL2_01 Missing 4 0 Zero 112 (72.7%)  24 (21.4%) >0 42 (27.3%) 12 (28.6%) IL8_01 Missing 4 0 Zero 99 (64.3%) 15 (15.2%) >0 55 (35.7%) 21 (38.2%) IL6_01 Missing 4 0 Zero 62 (40.3%) 10 (16.1%) >0 92 (59.7%) 26 (28.3%) LAMA3_01 Missing 4 0 Zero 148 (96.1%)  34 (23.0%) >0 6 (3.9%)  2 (33.3%) TP53_01 Missing 4 0 Zero 125 (81.2%)  22 (17.6%) >0 29 (18.8%) 14 (48.3%) CDKN1A_01 Missing 4 0 Zero 118 (76.6%)  22 (18.6%) >0 36 (23.4%) 14 (38.9%) CDKN2A_01 Missing 4 0 Zero 103 (66.9%)  26 (25.2%) >0 51 (33.1%) 10 (19.6%) TAZ_01 Missing 4 0 Zero 133 (86.4%)  29 (21.8%) >0 21 (13.6%)  7 (33.3%) LAMC1_01 Missing 136  33  Zero 19 (86.4%) 0 (0.0%) >0  3 (13.6%)  3 (100.0%) LAMB1_01 Missing 4 0 Zero 109 (70.8%)  16 (14.7%) >0 45 (29.2%) 20 (44.4%) LAMA1_01 Missing 4 0 Zero 128 (83.1%)  30 (23.4%) >0 26 (16.9%)  6 (23.1%) LAMC2_01 Missing 5 0 Zero 145 (94.8%)  33 (22.8%) >0 8 (5.2%)  3 (37.5%) LAMB3_01 Missing 4 0 Zero 139 (90.3%)  33 (23.7%) >0 15 (9.7%)   3 (20.0%) GDF15_01 Missing 28  4 Zero 65 (50.0%) 10 (15.4%) >0 65 (50.0%) 22 (33.8%) DSPP_01 Missing 73  13  Zero 16 (18.8%)  7 (43.8%) >0 69 (81.2%) 16 (23.2%) NTRK3_01 Missing 28  4 Zero 130 (100.0%) 32 (24.6%)

To formulate a model that distinguishes melanoma that presents with regional metastasis at the time of diagnosis vs. no metastasis, logic regression was used. Logic regression is a machine learning technique that uses Boolean explanatory variables. There was not a typical technique to create good cut points for logic regression. To assign cut points in the variables, recursive partitioning followed by standardization of cut point levels was used. These were arbitrarily set at 0, 50, 250, and 500. Cut points derived by logic regression were adjusted to the next highest standard level. The cut point for ITGB3 was maintained at 0. The selected model for predicting metastasis was the following:

IF(OR(ITGB3>0,(AND(OR(PTK2>250,PLAT>500,PLOD3>250),CDKN2A<50)))=TRUE then predict metastasis

Cut point ITGB3=0 Cut point PLAT=500 Cut point PTK2=250 Cut point PLOD3=250 Cut point CDKN2A=50

As can be seen from the formula, the risk of melanoma metastasis was high if ITGB3, PLAT, PTK2 or PLOD3 levels are increased and CDKN2A is low.

This model predicted regional metastasis (defined as a positive SLN biopsy at the time of primary cancer diagnosis) with a specificity of 80.3% and sensitivity of 97.3%.

Example 4—Use of Integrin Adhesions as a Biomarker of Melanoma Sentinel Lymph Node Metastasis Patient Sample Model Development Cohort

All patients with a diagnosis of malignant primary skin melanoma who had a SLN biopsy performed within 90 days of their diagnosis at Mayo Clinic Rochester, Mayo Clinic Arizona, or Mayo Clinic Florida were identified. The diagnosis of melanoma and all related histopathology data were established by ≧2 board-certified Mayo Clinic dermatopathologists. Patients evaluated at Mayo Clinic Rochester were excluded if they had denied access to their medical records for research purposes. The medical records were reviewed, and patients were excluded if they had a ‘thick’ melanoma (Breslow depth >4 mm; T classification T4). The following four variables were used to identify lesions of sufficient risk for inclusion: Breslow depth, presence of ulceration, mitotic rate >0 and age <40 years. A patient was included if i) Breslow depth >1 to <4 mm, or ii) Breslow depth between 0.75 and <1 mm with 1 or more of the other 3 risk factors, or iii) Breslow depth between 0.50 and <0.75 mm with 2 or more of the other 3 risk factors. Patients with ambiguous pathology or SLN biopsy findings were also excluded. The tissue blocks were reviewed, and patients were excluded if i) the blocks were not retrievable, or ii) sufficient material was not dispensable for research, or iii) only partial primary biopsy samples were available (i.e. biopsies with <80% of total Breslow depth), or iv) available tissue was limited to re-excision specimens in lieu of the original biopsy, or v) the quality of retrievable RNA was poor.

Model Validation Cohort

The model validation cohort consisted of patients who met the same criteria as described for the model development cohort. These patients had a SLN biopsy performed within 90 days of their diagnosis at either Mayo Clinic Rochester or Mayo Clinic Florida.

Data Collection

The following demographic, diagnosis, and pathologic information was abstracted from the medical record: gender, date of birth, date of malignant melanoma diagnosis, date of SLN biopsy, SLN biopsy finding, Breslow depth, mitotic rate (absent, 1-6, >6) presence of ulceration, presence of tumor invading lymphocytes, and presence of angiolymphatic invasion. For analysis purposes, Breslow depth was categorized using recent AJCC guidelines (Balch et al., J. Clin. Oncol., 27:6199 (2009)).

Block Processing

All tissue used was routinely processed, formalin-fixed and paraffin-embedded (FFPE). Preferred starting material for RNA purification was from freshly cut sections of FFPE tissue, each with a thickness of 20 μm. If a tissue was available only as unstained sections mounted on glass slides, RNA retrieval was attempted but typically yielded lower concentrations and poorer quality.

Microfluidic RT-PCR

The Fluidigm BioMark HD System was used for quantitative RT-PCR using EvaGreen DNA binding dye (Biotium) and 96.96 dynamic array integrated fluid circuits (Fluidigm). 77 specific targets in 62 genes (54 experimental and 8 control genes) were amplified per cDNA (standards, controls and experimental samples). Genes included: house-keeping (ACTB, RPLP0, RPL8), melanocyte lineage (MLANA, MITF, TYR, PMEL), basal keratinocyte lineage (KRT14), integrin cell adhesion receptors (ITGB1, ITGB3, ITGA2, ITGA3, ITGA5, ITGAV), integrin trafficking (SNX17, SNX31), fibronectin-related (FN1, THBS1, THBS2, THBS4, SPP1, PLAT, TNC, SPARC, POSTN, FBLN2, DSPP1), collagen-related (COL1A1, COL4A1, COL18A1, PLOD1, PLOD2, PLOD3, LOX, LOXL1, LOXL3, PCOLCE, PCOLCE2), laminins (LAMA1, LAMB1, LAMC1, LAMA3, LAMB3, LAMC2), other extracellular matrix (AGRN, VCAN, GDF15, BGAN, CTGF, CYR61, CSF2, CXCL1, CCL2, IL8, IL6), adhesion signaling (PTK2, CSRC), and cell cycle (CDKN1A, CDKN2A, TP53, YAP, TAZ). The following cDNA were run per array: standards, i.e. linearized cDNA mixes of targets ranging from 5 to 500,000 in copy number and prepared as 1:10 dilutions (a total of 6 standards), run in triplicates; control cDNA (nevi and melanoma metastases); experimental cDNA; the latter two were in duplicates. All cDNA was pre-amplified in a 14 cycle reaction (TaqMan Preamp Master Mix, Applied Biosystems). Array-based quantitative PCR was with the help of the TaqMan Gene Expression Master Mix (Applied Biosystems). After thermal cycling, raw Ct data was exported for further analysis. Standards were checked for linear amplification, i.e. a drop in Ct value by approximately log₂ 10 per 1:10 dilution. Copy numbers for negative and positive controls were normalized to 25,000 copies of total housekeeping genes. Averaged, normalized gene copy numbers were compared to an internal standard for inter-experiment variation. Data from arrays that did not pass both linear amplification and reproducibility checks were discarded.

To account for sample contamination from keratinocyte-derived RNA, the gene copy number of KRT14, a basal keratinocyte marker, was determined. This number was multiplied with a gene-specific contamination factor, i.e. a value of gene copy number contamination per copy of KRT14. Expression profiling of normal skin devoid of melanocyte nests was performed to establish a contamination factor. The calculated number of keratinocyte-derived RNA contamination was then deducted from the averaged, normalized gene copy number. The final averaged, normalized and background-corrected gene copy number was used for further analysis.

To assess for melanocyte content, at least two melanocyte lineage markers were amplified: MLANA and MITF. Sufficient melanocyte content was assumed if the sum of their averaged, normalized and background-corrected copy numbers was 1,000. If this was not the case, presence of melanocytic tumor had to be confirmed on tissue recuts followed by histologic review. Samples from tissue blocks exhausted of tumor were discarded. Expression data from samples that passed all quality controls were combined with pathology and clinical data and used for statistical modeling.

Chemicals, Antibodies and cDNA

Isopropyl β-D-1-thiogalactopyranoside (IPTG), 4′,6-Diamidino-2-phenylindole dihydrochloridemitomycin (DAPI), blebbistatin and PF-573228 were purchased from Sigma-Aldrich. Dabrafenib (GSK2118436) was purchased from Selleckchem. FAK antibody (06-543) was from EMD Millipore. FAK pY397 (44624G) antibody was from Life Technologies. Total ERK (9102) and phospho-ERK (4370) antibodies were from Cell Signaling. Paxillin (610051), ITGB3 (555754), ITGB1 (555443) and mouse IgG₁ kappa (555749) antibodies were from BD Transduction Labs. Drugs were used at 5 μM final concentration. EGFP control cDNA was from (Lonza). FAK cDNA was obtained from A. Huttenlocher, Addgene plasmid number 35039 (Chan et al., J. Biol. Chem., 285:11418-26 (2010)).

Cell Lines

WM858 were purchased from the Meenhard Herlyn lab (Wistar Institute). WM278 and WM1617 lines were purchased from Coriell Cell Repositories. KN lines were isolated from lymph node metastases using a gentle MACS dissociator and tumor dissociation kit (Miltenyi Biotec). WM and KN lines were propagated exclusively in vitro. M lines were isolated from melanoma brain metastases using previously described methods (Carlson et al., Curr. Prot. Pharmacology, 14.6.1-14.6. 23 (2011)). Some M lines were propagated in mice. Cells were cultured in vitro using DermaLife M Medium (Lifeline Cell Technology).

Generation of IPTG-Inducible FAK shRNA Cells

Five TRC clones were cloned into the pLKO-puro-IPTG-1XLacO vector. The same vector format was used for the non-target negative control (NC) shRNA SHC312V (Sigma-Aldrich). TRC identifiers were as follows: TRCN0000121207, TRCN0000121318, TRCN0000121129, TRCN0000194984, and TRCN0000196310. Lentivirus was produced for each TRC clone and multiple pools of WM858 cells were transduced per clone. The first three TRC sequences did not induce significant FAK knockdown in WM858 cells. The latter two (abbreviated as shRNA 841 and 102) were effective and used for experiments. Selection of successfully transduced cells was with puromycin (Sigma-Aldrich).

Focal Adhesion Visualization on Fibronectin Micropatterns

Cells were plated on micropatterned disks of fluorescent fibronectin surrounded by a cytophobic surface (CYTOO). Cells were allowed to adhere for 1 hour in serum-free medium, and then were fixed and incubated with anti-paxillin antibody followed by a fluorescent secondary antibody and DAPI. Images of fluorescent cells were obtained with a laser scanning confocal microscope (Zeiss LSM780). Max intensity overlays of 15 representative cells per cell type were generated using a plug-in ImageJ macro from CYTOO.

Cell Proliferation

Automated quantification of cell proliferation was by the IncuCyte™ kinetic imaging system (Essen Bioscience). Approximately 2,000 cells were seeded into a 96 well cell culture dish, 8 replicates per condition over the indicated time. Data analysis was with the IncuCyte ZOOM software package.

Western Blotting by Protein Simple

Western blotting was by standard techniques or automated with a Wes device from ProteinSimple. The automated work-flow was according to the manufacturer's instructions. Image analysis was with the ProteinSimple Compass software.

Gene Expression by Next-Generation Sequencing

Sequencing of FFPE-derived RNA was performed using standard methods. Briefly, RNA-derived cDNA libraries were prepared using the NuGen Ovation® RNAseq FFPE library system. Concentration and size distribution of the resulting libraries were determined on an Agilent Bioanalyzer DNA 1000 chip and confirmed by Qubit fluorometry (Life Technologies, Grand Island, N.Y.). Unique indexes were incorporated at the adaptor ligation phase for 3-plex sample loading. Libraries were loaded onto paired end flow cells to generate cluster densities of 700,000/mm² following Illumina's standard protocol. The flow cells were sequenced as 51×2 paired end reads on an Illumina HiSeq 2000. For differential gene expression analysis, the edgeR bioconductor software package (http://bioconductor.org) was used. Because scaling by total lane counts (e.g., by the ‘reads per kilobase of exon model per million mapped reads’ (RPKM) method) can bias estimates of differential expression, quantile-based normalization was used on read counts to determine if genes are differentially expressed (Bullard et al., BMC bioinformatics, 11:94 (2010)) using the negative binomial method (Anders and Huber, Genome Biol., 11:R106 (2010)) requiring an adjusted p-value of <0.01 controlled for multiple testing using the Benjamini and Hochberg correction.

Statistical Methods Model Development

The primary outcome measure for this study is a positive SLN within 90 days of the primary melanoma diagnosis. Clinical and pathologic characteristics were evaluated univariately for an association with SLN positivity using the chi-square test for categorical variables and the two-sample t-test for continuous variables. A prediction model was constructed from these characteristics using multivariable logistic regression. Associations were summarized using the odds ratio (OR) and corresponding 95% confidence intervals (CI) derived from the model estimates.

When evaluating gene expression data as potential predictors of outcomes, it is useful to model interactions between the genes. Logic regression can be used to discover and model interactions of binary explanatory variables, and combinations are created using Boolean operators (‘and’, ‘or’ and ‘not’) (Ruczinski et al., J. Comput. Graph. Stat., 12:475-511 (2003)). Since logic regression is limited to using binary explanatory variables, reasonable cutoff values needed to be established for each of the 54 experimental genes. For each gene, a separate Classification and Regression Tree (CART) model was fit to identify the best gene expression cutoffs to differentiate between patients with positive and negative SLN using the Gini rule for splitting, prior probabilities proportional to the observed data frequencies, and 0/1 losses. The AUC for these models ranged from 0.50 to 0.781. A total of 147 binary variables were created using all the breakpoints generated by the CART models and these breakpoints were then used to fit the logic regression.

Receiver operating characteristic (ROC) curves were constructed for the final prediction models. The predictive ability of each model was summarized by the area under curve (AUC), and the AUC estimates were compared between models using the DeLong, DeLong, and Clarke-Pearson non-parametric method for comparing the AUC for correlated ROC curves.

Model Validation

The performance of the prognostic model developed using the development cohort was validated in a new cohort by assessing the discrimination and calibration. Discrimination was assessed by quantifying the model's ability to discriminate between patients in the new cohort who do and do not have a positive SLNB using the area under the ROC curve. Calibration was assessed by grouping patients into 5 quintiles based on their predicted probabilities estimated by the model and comparing the median predicted probability in each quintile with the observed proportion of patients with a positive SNLB in that quintile.

The statistical analysis was performed SAS version 9.2 and R version 3.0.1. The CART analysis was performed using the rpart package (rpart: Recursive Partitioning. http://CRAN.R-project.org/package=rpart. Version 4.1-1; Therneau and Atkinson. An introduction to recursive partitioning using the RPART routines: Technical Report 61, Section of Biostatistics, Mayo Clinic, Rochester). The logic regression used LogicReg package (LogicReg: Logic Regression. http://CRAN.R-project.org/package=LogicReg. Version 1.5.5; Ruczinski et al., J. Comput. Graph. Stat., 12:475-511 (2003)).

Logic Regression Logic regression fits regression models using 1 to 5 trees, and the trees can be composed of many leaves. Simulated annealing was used to explore possible logic regression models to find a good model. The technique starts by fitting a model built randomly using a specified number of leaves and trees. A new model is created by randomly permuting the current model by changing a leaf or Boolean operator. The performance of the current model is then compared to the new model. If the new model performs better, then it becomes the current model, and the process is repeated. Simulated annealing avoids local optima by controlling when inferior models were chosen. The null model randomization test was used to determine if there was a relationship between the 147 binary gene expression variables and SLN positivity. The optimal number of leafs and trees was determined using cross validation and permutation techniques.

The null model randomization test was used to determine if there was a relationship between the 147 binary gene expression variables and SLN positivity. First, the best model was fit for all biopsy samples using logic regression. Next, the SLN positivity outcome for all the patients was randomly reassigned and fit another model. The process of randomly reassigning the SLN positivity outcome and fitting a model was performed 25 times. FIG. 8A shows the histogram of the deviance scores from the models built using the randomized outcomes. The null model randomization test demonstrated there was a relationship between SLN positivity and gene expression since the deviance scores were all worse than the best model deviance scores.

The optimal number of leafs and trees in the logic regression model was determined using cross validation and permutation techniques. Ten-fold cross validation was used to help determine the ideal model size given the data. FIG. 8B shows the deviance score for the test samples for different model configurations. The label in the square represents the number of trees used in the model. The x-axis indicates the number of binary variables or leaves used in the model. The best deviance score was obtained using a 2 tree model using 4 binary explanatory variables. When more than 4 explanatory variables were used in the model, there may be an over-fitting issue since the test data deviance scores degrade when there are more than 4 explanatory variables. The permutation test was also used to confirm ideal model size given the data. The permutation test fits the best model given the model size. In each tree the binary variables are put together using ‘and’, ‘or’ and ‘not’. It follows that each logic tree has a binary outcome. For a model having n trees the sample could be partitioned into 2n groups. With 2 trees, the sample was partitioned into 4 groups. The SLN outcomes were permuted by randomly reassigning the outcome within each of the 4 groups. The model was refit based on permuted data. Notice that the exact same model can be found within the permuted data. Models scoring better than the best model were likely because of fitting on noise. Models scores worse than the best model were likely caused by the model being too small. FIG. 8C shows this process repeated 1,000 times for each model size. Most of the permuted models with two leaves performed worse than the best model, indicating a larger model would be optimal. About 10% of the models using 5 leaves fit using permuted data outperformed the best model. It was recommended to choose the model size where the permuted outcome variables outperform the best model 5% to 20% of the time (Ruczinski et al., J. Comput. Graph. Stat., 12:475-511 (2003)). The cross validation test and the permutation test indicate that the optimum model size was two trees using 4 or 5 binary variables. The formulas for the best fitting models involved two trees with a model size of 4 or 5 (FIG. 8D). The best 4 leaf model considered β3 integrin (ITGB3), cellular tumor antigen p53 (TP53), the laminin B1 chain (LAMB1), and tissue-type plasminogen activator (PLAT). The best 5 leaf model considered the same 4 genes plus agrin (AGRN). Notice that the composition for one tree was exactly the same for both models ((LAMB1 >250) or

(PLAT >427)).

Results

This investigation started by identifying functional networks of differentially expressed genes in benign melanocytic lesions vs. invasive melanoma. In a pilot study, three patients with benign nevi were age and gender-matched one-to-one to a patient with a primary skin melanoma that had metastasized regionally. A total of 15,413 genes were identified and measured by next-generation sequencing (NGS) of patient biopsy-derived RNA. Differential gene expression analysis yielded 160 genes with a false-discovery rate (FDR)<0.01. These were entered into the STRING database to identify functional gene networks. Genes that were without known functional relationships to other genes were hidden. Two clusters with more than two nodes emerged; the largest was linked to integrin cell adhesion (FIG. 9). Within that cluster, β3 integrin (ITGB3) had the lowest FDR and the highest connectivity.

Next, the objective was to confirm that genes involved in integrin cell adhesion are up-regulated in invasive melanoma. A test set of 73 benign nevi (53 were without histological atypia, 7 were with mild, 11 with moderate and 2 with severe atypia), 38 primary skin melanoma that had metastasized regionally (median Breslow depth of 3 mm; IQR, 2 to 4 mm), and 11 in-transit regional melanoma metastases was assembled. A method for determining copy number of 77 specific targets in 62 genes (54 experimental and 8 control genes) by quantitative PCR was established as described herein. Genes were categorized as follows: i) integrin adhesion receptor subunits; ii) FN1 and related extracellular matrix (ECM) components; iii) collagen genes and enzymes that facilitate the cross-linking of collagens; iv) laminin subunits; v) other ECM components including those of a pro-inflammatory DNA damage response (Coppe et al., PLoS biology, 6:e301 (2008)); vi) integrin-activated kinases, and vii) cell cycle related. Genes with significant regulation between benign and malignant were mainly in the categories of integrins and FN1 and related ECM components, thus confirming NGS results (FIG. 10). β3 integrin was with the highest fold change of all tested integrin subunits.

The following was performed to assess whether adhesion gene expression in tissue sections predicted metastasis to SLN and to determine whether the method outperformed the current clinical gold standard for predicting metastasis risk, i.e. Breslow invasion depth (Breslow, Annals of Surg., 172:902 (1970)). The model development cohort consisted of a total of 360 thin and intermediate thickness primary melanoma (Breslow depth ≦4 mm) of all histologic types with a SLN biopsy within 90 days of their diagnosis (Table 14). To exclude minimal risk lesions, thin melanoma (Breslow depth <1 mm; T classification 1) without additional risk factors (ulceration, mitoses, patient age <40) were not considered. Thick melanoma (Breslow depth >4 mm; T classification 4) were excluded because they frequently metastasize to SLN and the clinical utility of a molecular test is low.

TABLE 14 Summary of histologic types of melanoma that triggered a SLN biopsy. Histologic Type No. (%) Superficial Spreading 180 (50.0%) Nodular 70 (19.4%) Unclassifiable 30 (8.3%) Desmoplastic 16 (4.4%) Lentigo Maligna 15 (4.2%) Spindled 13 (3.6%) Acral Lentiginous 9 (2.5%) Spitzoid 4 (1.1%) Nevoid 3 (0.8%) Dermal 1 (0.3%) Not documented 19 (5.3%)

Table 15 summarizes the clinical and pathologic factors that were evaluated univariately for an association with SLN positivity. Ulceration, Breslow depth, and age were identified as independently associated with SLN positivity (Table 16, Model A). Logic regression models were fit utilizing 147 binary variables derived from 54 experimental genes and evaluated using the breakpoints generated by the CART models for the 54 genes. The best 4 leaf model considered 3 integrin (ITGB3), cellular tumor antigen p53 (TP53), the laminin B1 chain (LAMB1), and tissue-type plasminogen activator (PLAT). SLN positivity within each of these 4 categories is summarized at the bottom of Table 15. The model results for a combined model including both the clinical/pathologic factors and the gene expression parameters are presented as model B in Table 16.

TABLE 15 Summary of the association of clinical and pathologic factors with SLN positivity based on 360 SLN biopsies. Chi-square Positive SLNB test Factor N (%) p value Gender 0.84 Male (N = 225) 47 (20.9%) Female (N = 135) 27 (20.0%) Age (years) <0.001 16-<40 (N = 55) 16 (29.1%) 40-<59 (N = 112) 33 (29.5%) 60+ (N = 193) 25 (13.0%) Ulceration <0.001 No (N = 295) 50 (16.9%) Yes (N = 65) 24 (36.9%) Breslow depth (mm) <0.001 0.50-1 (N = 93) 6 (6.4%) 1.01-2 (N = 177) 31 (17.5%) 2.01-4 (N = 90) 37 (41.1%) Mitotic rate  0.12 † Missing (N = 14)  4 Absent (N = 42) 4 (9.5%) 1-6 (N = 246) 51 (20.7%) >6 (N = 58) 15 (25.9%) Tumor invading lymphocytes  0.37 † Missing (N = 31) 12 No (N = 86) 19 (22.1%) Yes (N = 243) 43 (17.7%) Angiolymphatic invasion 0.28 No (N = 344) 69 (20.1%) Yes (N = 16) 5 (31.3%) 4-level gene score <0.001 A: NOT (lamb1 > 250 or plat > 427) 10 (4.2%) and NOT (itgb3 > 10 and tp53 ≦ 50) (N = 237) B: (lamb1 > 250 or plat > 427) 26 (38.2%) but NOT (itgb3 > 10 and tp53 ≦ 50) (N = 68) C: (itgb3 > 10 and tp53 ≦ 50) 18 (52.9%) but NOT (lamb1 > 250 or plat > 427) (N = 34) D: (lamb1 > 250 or plat > 427) AND 20 (95.2%) (itgb3 > 10 and tp53 ≦ 50) (N = 21) † P-values were calculated based on the subset of patients with non-missing values.

TABLE 16 Multivariable logistic regression analyses of characteristics associated with SLN positivity. Model A Model B Model C Adjusted OR Adjusted OR Adjusted OR Factor (95% CI) p-value (95% CI) p-value (95% CI) p-value Ulceration 0.026 0.25 0.39 No Referent Referent Referent Yes 2.11 (1.10, 4.06) 1.58 (0.73, 3.44) 1.38 (0.66, 2.88) Breslow depth (mm) <0.001 0.13 0.036 0.50-1 Referent Referent Referent 1.01-2 3.33 (1.31, 8.44) 1.28 (0.46, 3.59) 1.50 (0.54, 4.21) 2.01-4 11.46 (4.34, 30.27) 2.52 (0.84, 7.58) 3.30 (1.11, 9.77) Patient age (years) <0.001 0.001 0.001 16-<40 3.85 (1.75, 8.50)  6.18 (2.24, 17.06)  5.14 (1.99, 13.25) 40-<59 3.47 (1.83, 6.59) 2.92 (1.31, 6.52) 2.91 (1.41, 6.00) 60+ Referent Referent Referent Gene score <0.001 <0.001 A — Referent Referent B — 13.17 (5.53, 31.39) C — 12.27 (4.52, 33.33) {close oversize brace} 17.32 (8.02, 37.41) D — 236.60 (36.95, >999) 

It was subsequently decided to collapse the four categories in the gene model into two categories which yielded a simpler model without loss of overall predictive ability (Table 16, model C). The receiver operating characteristic (ROC) curves for the three models are displayed in FIG. 11A. The overall predictive ability of the combined model as measured by the area under the curve (AUC) or c-index was significantly greater for model C compared to model A (0.89 vs. 0.77, p<0.001). FIG. 11B depicts the sensitivity and specificity of model C as the level of the predicted probability of a positive SLNB used to define a positive test was varied. A predicted probability of 0.255 corresponds to a sensitivity and specificity of 82%. A nomogram constructed from model C is presented in FIG. 11C. For a given patient, points were assigned to each of the variables, and a total score was derived. The total points score corresponded to a predicted probability of positive SLN biopsy.

The model validation cohort included 104 patients. Table 17 summarizes the association of the clinical and pathologic factors with SLN positivity, separately for the two cohorts. The discriminative ability of the predictive model was excellent when applied to the validation cohort (AUC 0.92, 95% CI 0.87-0.97). Table 18 compares the predicted and observed rate of positive SLNB for the 5 quintiles defined by the distribution of the predicted probabilities. The two rates track consistently across the 5 quintiles suggesting reasonable calibration.

TABLE 17 Summary of the association of clinical and pathologic factors with SLN positivity, separately for the model development and model validation cohorts. Model development cohort Model validation cohort Positive Positive Factor SLNB N (%) p value† SLNB N (%) p value† Gender 0.84 0.54 Male 47/225 (20.9) 22/78 (28.2) Female 27/135 (20.0) 9/26 (34.6) Age (years) <0.001 0.17 16-<40 16/55 (29.1) 3/5 (60.0) 40-<59 33/112 (29.5) 12/34 (35.3) 60+ 25/193 (13.0) 16/65 (24.6) Ulceration <0.001 0.35 No 50/295 (16.9) 23/83 (27.7) Yes 24/65 (36.9) 8/21 (38.1) Breslow depth (mm) <0.001 0.035 0.50-1 6/93 (6.4) 5/28 (17.9) 1.01-2 31/177 (17.5) 10/41 (24.4) 2.01-4 37/90 (41.1) 16/35 (45.7) Mitotic rate 0.12 0.21 Missing  4/14 5/14 Absent 4/42 (9.5) 0/7 (0.0) 1-6 51/246 (20.7) 21/68 (30.9) >6 15/58 (25.9) 5/15 (33.3) Tumor invading lymphocytes 0.37 0.011 Missing 12/31 8/26 No 19/86 (22.1) 10/19 (52.6) Yes 43/243 (17.7) 13/59 (22.0) Angiolymphatic invasion 0.28 0.89 No 69/344 (20.1) 30/101 (29.7) Yes 5/16 (31.3) 1/3 (33.3) 4-level gene score <0.001 <0.001 A: NOT (lamb1 > 250 or plat > 427) 10/237 (4.2%) 1/63 (1.6) and NOT (itgb3 > 10 and tp53 ≦ 50) B: (lamb1 > 250 or plat > 427) 26/68 (38.2%) 18/25 (72.0) but NOT (itgb3 > 10 and tp53 ≦ 50) C: (itgb3 > 10 and tp53 ≦ 50) 18/34 (52.9%) 3/4 (75.0) but NOT (lamb1 > 250 or plat > 27) D: (lamb1 > 250 or plat > 427) AND 20/21 (95.2%) 9/12 (75.0) (itgb3 > 10 and tp53 ≦ 50) †P-values were calculated using the chi-square rest based on the subset of patients with non-missing values.

TABLE 18 Summary of the predicted and observed rate of positive SLNB for the 5 quintiles defined by the distribution of the predicted probabilities. Quintile defined based on Predicted rate of Observed rate the predicted probability positive SLNB† of positive SLNB ≦0.02 1.4% 0/15 (0%) >0.02-0.04 2.1% 0/23 (0%) >0.04-0.16 5.9% 0/24 (0%) >0.16-0.45 30.7%  17/22 (77.3%)  >0.45 65.4%  14/20 (70.0%) †Median predicted probability in each quintile

The following was performed to determine whether the expression of (33 integrin and other adhesion-related genes in melanoma is influenced by focal adhesion kinase (FAK), a key transducer of integrin signals and novel cancer therapy target (Infante et al., J. Clin. Oncol., 30:1527-33 (2012)). To test whether FAK controls adhesion gene expression, B-rafV600E WM858 cells were engineered to contain IPTG-inducible short hairpin RNA (shRNA) against FAK. FAK knock-down was highly effective at the RNA and protein level at concentrations equal to or exceeding 0.025 mM IPTG (Figure S3A-B). FAK could not be visualized in focal adhesions after 0.05 mM IPTG for 5 days (FIG. 12C-D). PYK2, a FAK-like tyrosine kinase, could not be detected in WM858 cells, irrespective of endogenous FAK levels. WM858 cells carrying control (NC) or FAK-specific shRNAs (841 and 102) were then exposed to IPTG for 5 days, and changes in adhesion gene expression were subsequently quantified by PCR. FAK-specific shRNA increased β3 integrin expression 2-fold (FIG. 13A). Vice versa, over-expression of a FAK cDNA led to a 2-fold down-regulation of (33 integrin and other integrin subunits (FIG. 13B). At the protein level, FAK knock-down led to an increase in cell surface β3 integrin (FIG. 13C-D), which was accompanied by a noticeable increase in focal adhesion size and number (FIG. 13E). An increase in proliferation was observed when FAK levels were reduced (FIG. 13F-G) as was a faster scratch wound healing (FIG. 13H-I). In line with these data, FAK knock-down was found to induce extracellular regulated kinases (ERK) activity (FIG. 13J-K). FAK inhibition by the small molecule FAK kinase inhibitor PF-573228 or blebbistatin, a drug that inhibits FAK by blocking myosin II-dependent contractile forces (Seo et al., Biomaterials, 32:9568-75 (2011)), induced β3 and also β1 integrin surface levels in B-rafV600E, but not B-raf wild-type melanoma cells (FIG. 13L). In contrast, Dabrafenib, a B-raf inhibitor and established single agent therapeutic of metastatic melanoma, reduced integrin levels in most melanoma cells but not in NHM (FIG. 13L). While FAK inhibitors effectively suppressed FAK tyrosine (Y) 397 auto-phosphorylation in melanoma cells, Dabrafenib increased FAK phosphorylation (FIG. 13M), suggesting that in melanoma B-raf promotes integrin expression by inhibiting FAK, which in turn provides a scaffold for active ERK. In line with this hypothesis, PF-573228 was found to induce ERK activity (FIG. 13M-O). Moreover, Dabrafenib-induced blockage of ERK activity could be partially reversed by a complete FAK knock-down in 102 cells (FIG. 13P).

As described herein, a completely customizable high-density microfluidic PCR platform was used to allow for the quantification of multiple genes by repeat measurements. For example, at least 26 individual PCR reactions were performed per patient sample to measure house-keeping genes. To account for RNA contamination by basal keratinocytes—a cell type with stem cell-like features and high levels of adhesion gene expression—keratin 14 (KRT14), a basal keratinocyte marker, was quantified. KRT14 copy number was multiplied with a gene specific, per-copy-of-KRT14 contamination factor that was pre-determined by analyzing normal skin; and the product of this calculation was used to correct for keratinocyte background. In addition, melanocyte markers were routinely assayed to quantify melanocyte content in processed tissue. Aside from throughput, the methods provided herein have several other advantages. First, they are quantitative. This is an advantage over IHC or fluorescent in-situ hybridization (FISH), where the signal intensity is difficult to normalize and/or image analysis is subjective and time consuming. Second, they are based on the quantitation of RNA, which in contrast to DNA carries epigenetic information. Third, they are devoid of array-based hybridization steps which can lead to hybridization errors and noise. Fourth, they are easily adjusted to include additional genes of interest.

The results provided herein demonstrate that the best 4 leaf molecular model for predicting SLN metastasis considered β3 integrin, the laminin B1 chain, tissue-type plasminogen activator and tumor antigen p53. The overall predictive ability of a combined model that included molecular parameters was significantly greater than a model that only included clinical/pathologic factors (0.89 vs. 0.77, p<0.001).

The results provided herein also demonstrate that FAK inhibition induces the expression of integrins, induces the size of focal adhesions, and stimulates proliferation and mitogen activated kinases. These effects were strongest in B-raf^(V600E) cells, likely because mutant B-raf inhibits FAK to trigger integrin expression.

The 2-tree 2-leaf model was generated using logic regression and slow cooling on simulated annealing parameters.

Additional analysis of samples by next generation sequencing using a cohort of four patients with primary skin melanoma that had not metastasized (median Breslow depth: 2.6 mm) and three patients that had metastasized regionally (median Breslow depth: 2.3 mm) yielded a total of 208 differentially regulated genes out of a total of 15,196 measured genes. ITGB3 as well as SRC, a key downstream effector of β3 integrin, formed the center of a functional network deregulated in regionally metastatic vs. non-metastatic melanoma (FIGS. 16 and 17).

Expanding the sample size of the model validation cohort from 104 to 146 resulted in excellent discriminative ability of the clinicopathologic+molecular model with an AUC of 0.93, 95% CI 0.87-0.97 (FIG. 17). Using the suggested cutoff of 10% (Balch et al., J. Am. Acad. Dermatol., 60:872-875 (2009)), the false positive rate was 22%, and the false negative rate was 0%. These results demonstrate that data obtained by gene expressing profiling can be combined with Breslow depth, tumor ulceration, and patient age to calculate the predicted probability of SLN positivity at the time of primary diagnosis. These results also can be used to improve patient care by avoiding unnecessary SLN procedures.

Example 5—Identifying Inhibitors of Integrin Cell Adhesion Remodeling

Osteopontin (SPP1) is a proto-typical cancer-associated extracellular matrix gene and ligand of αv and α5β1 integrins. SPP1 is highly overexpressed in melanoma (Talantov et al., Clin. Cancer Res., 11:7234-42 (2005)) and its upregulation correlates with metastasis risk (Conway et al., Clin. Cancer Res., 15:6939-6946 (2009) and Mitra et al., Br. J. Cancer, 103:1229-1236 (2010)). To rapidly screen chemical compounds for their ability to inhibit SPP1 expression in vitro, the endogenous SPP1 promoter of WM858 melanoma cells was tagged with a dual luciferase system using zinc finger nucleases. The SPP1-promoter drives firefly luciferase tagged with a protein degradation sequence (hPEST). A CMV-promoter driven renilla luciferase was used as a loading control (FIG. 14). Assaying both luciferase signals was fast and amendable to high-throughput screening.

The investigation was started by screening a 1280 compound library of pharmaceutically active compounds (LOPAC; Sigma-Aldrich). The firefly signal was first normalized to the renilla signal, then to DMSO-treated control wells (FIG. 15A). Normalized ratios <0.25 were observed for a handful of compounds, including Pentamidine (FIG. 15B). Pentamidine is an FDA approved antimicrobial drug that is used in the prevention and treatment of Pneumocystis pneumonia. It appears to possess other activities as well. See, e.g., Pathak et al., Molecular Cancer Therapeutics, 1:1255-1264 (2002); Smith et al., Anti-Cancer Drugs, 21:181 (2010); and Sun and Zhang, Nucleic Acids Res., 36:1654-1664 (2008).

Pentamidine exhibited little cytotoxicity in WM858 and M12 cells with ED₅₀'s >100 μM (M12 cells are metastatic B-raf^(V600E) melanoma cells that were recently established from a patient). Pentamidine inhibited SPP1 mRNA in both WM858 (FIG. 15C) and M12 cells (FIG. 15D). Pentamidine also reduced expression of β integrin and t-PA (PLAT) (FIG. 15E). Next, a red-fluorescent nuclear protein was stably expressed in M12 cells to automatically count nuclei over time (using the IncuCyte ZOOM system, Essen Bioscience), a surrogate measure of cell proliferation. Pentamidine reduced M12 proliferation with an ED₅₀ of 40 μM. When M12 cells were allowed to migrate into Matriger-embedded scratch wounds, Pentamidine inhibited Matrigel® invasion more effectively than Dabrafenib (FIG. 15F-G).

To determine whether Pentamidine reduces SPP1 expression in vivo, M12 cells were injected intradermally into female nude mice and left to grow until xenograft tumors formed (FIG. 15H-I). Then, four different doses of Pentamidine were injected intramuscularly (i.m.) into groups of three mice for six consecutive days. It was previously shown that serum concentration in patients injected with 4 mg/kg Pentamidine i.m. daily (FDA labeling) range from 0.2-1.4 μg/mL (0.3-2.4 μM). In rats, slightly lower levels can be achieved using 10 mg/kg i.m. daily (0.1-0.4 μg/mL) (Bernard et al., J. Inf. Dis., 152:750-754 (1985) and Waalkes et al., Clin. Pharma. Therap., 11:505-512 (1969)). In the current study, at the highest Pentamidine dose (80 mg/kg/daily), all mice died. Mice survived at 40 mg/kg/day, but appeared sick. The other two doses, specifically the 8 mg/kg/day dose, were well tolerated. Tumors were subsequently harvested, lysed, and analyzed by quantitative PCR. All doses of Pentamidine led to a reduction of SPP1, β3 integrin, and t-PA (PLAT) mRNA expression in tumor tissue (FIG. 15J).

These results demonstrate that pentamidine can be used to reduce the expression of ITGB3, PLAT, and SPP1.

OTHER EMBODIMENTS

It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims. 

1. A method for identifying a metastatic malignant skin lesion, wherein said method comprises: (a) determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, ITGB3, LAMB1, and TP53 to obtain a measured expression level of said marker gene for said test sample, (b) determining, within said test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of said keratinocyte marker gene for said test sample, (c) removing, from said measured expression level of said marker gene for said test sample, a level of expression attributable to keratinocytes present in said test sample using said measured expression level of said keratinocyte marker gene for said test sample and a keratinocyte correction factor to obtain a corrected value of marker gene expression for said test sample, and (d) identifying said test sample as containing a metastatic malignant skin lesion based, at least in part, on said corrected value of marker gene expression for said test sample.
 2. The method of claim 1, wherein said keratinocyte marker gene is K14.
 3. The method of claim 1, wherein said marker gene is PLAT or ITGB3.
 4. The method of claim 1, wherein step (c) comprises (i) multiplying said measured expression level of said keratinocyte marker gene for said test sample by said keratinocyte correction factor to obtain a correction value and (ii) subtracting said correction value from said measured expression level of said marker gene for said test sample to obtain said corrected value of marker gene expression for said test sample.
 5. The method of claim 1, wherein said method comprises determining, within said test sample, the expression level of at least two marker genes selected from the group consisting of PLAT, ITGB3, LAMB1, and TP53 to obtain measured expression levels of said at least two marker genes for said test sample.
 6. The method of claim 1, wherein said method comprises determining, within said test sample, the expression level of at least three marker genes selected from the group consisting of PLAT, ITGB3, LAMB1, and TP53 to obtain measured expression levels of said at least three marker genes for said test sample.
 7. The method of claim 1, wherein said method comprises determining, within said test sample, the expression level of PLAT, ITGB3, LAMB1, and TP53 to obtain measured expression levels of said PLAT, ITGB3, LAMB1, and TP53 for said test sample.
 8. A kit for identifying a metastatic malignant skin lesion, wherein said kit comprises: (a) a primer pair for determining, within a test sample, the expression level of a marker gene selected from the group consisting of LAMB1 and TP53 to obtain a measured expression level of said marker gene for said test sample, and (b) a primer pair for determining, within said test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of said keratinocyte marker gene for said test sample.
 9. The kit of claim 8, wherein said keratinocyte marker gene is K14.
 10. The kit of claim 8, wherein said marker gene is LAMB1.
 11. The kit of claim 8, wherein said marker gene is TP53.
 12. The kit of claim 8, wherein said kit comprises primer pairs for determining, within said test sample, the expression level of LAMB1 and TP53 to obtain measured expression levels of said LAMB1 and TP53 for said test sample.
 13. The kit of claim 8, wherein said kit comprises primer pairs for determining, within said test sample, the expression level of PLAT to obtain measured expression levels of said PLAT for said test sample.
 14. The kit of claim 8, wherein said kit comprises primer pairs for determining, within said test sample, the expression level of ITGB3 to obtain measured expression levels of said ITGB3 for said test sample.
 15. The kit of claim 8, wherein said kit comprises primer pairs for determining, within said test sample, the expression level of PLAT and ITGB3 to obtain measured expression levels of said ITGB3 and PLAT for said test sample.
 16. A method for identifying a metastatic malignant skin lesion, wherein said method comprises: (a) determining, within a test sample, the expression level of a marker gene selected from the group consisting of PLAT, ITGB3, LAMB1, and TP53 to obtain a measured expression level of said marker gene for said test sample, (b) determining, within said test sample, the expression level of a keratinocyte marker gene to obtain a measured expression level of said keratinocyte marker gene for said test sample, (c) removing, from said measured expression level of said marker gene for said test sample, a level of expression attributable to keratinocytes present in said test sample using said measured expression level of said keratinocyte marker gene for said test sample and a keratinocyte correction factor to obtain a corrected value of marker gene expression for said test sample, and (d) identifying said test sample as containing a metastatic malignant skin lesion based, at least in part, on said corrected value of marker gene expression for said test sample.
 17. The method of claim 16, wherein said keratinocyte marker gene is K14.
 18. The method of claim 16, wherein said marker gene is LAMB1 or TP53.
 19. The method of claim 16, wherein step (c) comprises (i) multiplying said measured expression level of said keratinocyte marker gene for said test sample by said keratinocyte correction factor to obtain a correction value and (ii) subtracting said correction value from said measured expression level of said marker gene for said test sample to obtain said corrected value of marker gene expression for said test sample. 20-29. (canceled) 